{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [], "gpuType": "T4", "toc_visible": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "source": [ "## 1.prepare" ], "metadata": { "id": "VyZYU9qVHby8" } }, { "cell_type": "code", "source": [ "!sudo apt install tesseract-ocr\n", "!pip install pytesseract" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MG54zqy9rK85", "outputId": "a74411b7-964c-4980-bfa4-5cec39786c4e" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Reading package lists... Done\n", "Building dependency tree... Done\n", "Reading state information... Done\n", "The following additional packages will be installed:\n", " tesseract-ocr-eng tesseract-ocr-osd\n", "The following NEW packages will be installed:\n", " tesseract-ocr tesseract-ocr-eng tesseract-ocr-osd\n", "0 upgraded, 3 newly installed, 0 to remove and 16 not upgraded.\n", "Need to get 4,816 kB of archives.\n", "After this operation, 15.6 MB of additional disk space will be used.\n", "Get:1 http://archive.ubuntu.com/ubuntu jammy/universe amd64 tesseract-ocr-eng all 1:4.00~git30-7274cfa-1.1 [1,591 kB]\n", "Get:2 http://archive.ubuntu.com/ubuntu jammy/universe amd64 tesseract-ocr-osd all 1:4.00~git30-7274cfa-1.1 [2,990 kB]\n", "Get:3 http://archive.ubuntu.com/ubuntu jammy/universe amd64 tesseract-ocr amd64 4.1.1-2.1build1 [236 kB]\n", "Fetched 4,816 kB in 1s (3,510 kB/s)\n", "debconf: unable to initialize frontend: Dialog\n", "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78, <> line 3.)\n", "debconf: falling back to frontend: Readline\n", "debconf: unable to initialize frontend: Readline\n", "debconf: (This frontend requires a controlling tty.)\n", "debconf: falling back to frontend: Teletype\n", "dpkg-preconfigure: unable to re-open stdin: \n", "Selecting previously unselected package tesseract-ocr-eng.\n", "(Reading database ... 120893 files and directories currently installed.)\n", "Preparing to unpack .../tesseract-ocr-eng_1%3a4.00~git30-7274cfa-1.1_all.deb ...\n", "Unpacking tesseract-ocr-eng (1:4.00~git30-7274cfa-1.1) ...\n", "Selecting previously unselected package tesseract-ocr-osd.\n", "Preparing to unpack .../tesseract-ocr-osd_1%3a4.00~git30-7274cfa-1.1_all.deb ...\n", "Unpacking tesseract-ocr-osd (1:4.00~git30-7274cfa-1.1) ...\n", "Selecting previously unselected package tesseract-ocr.\n", "Preparing to unpack .../tesseract-ocr_4.1.1-2.1build1_amd64.deb ...\n", "Unpacking tesseract-ocr (4.1.1-2.1build1) ...\n", "Setting up tesseract-ocr-eng (1:4.00~git30-7274cfa-1.1) ...\n", "Setting up tesseract-ocr-osd (1:4.00~git30-7274cfa-1.1) ...\n", "Setting up tesseract-ocr (4.1.1-2.1build1) ...\n", "Processing triggers for man-db (2.10.2-1) ...\n", "Collecting pytesseract\n", " Downloading pytesseract-0.3.10-py3-none-any.whl (14 kB)\n", "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from pytesseract) (23.1)\n", "Requirement already satisfied: Pillow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from pytesseract) (9.4.0)\n", "Installing collected packages: pytesseract\n", "Successfully installed pytesseract-0.3.10\n" ] } ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "F4iTnkNCFV1m" }, "outputs": [], "source": [ "import os\n", "import torch\n", "from torch import nn\n", "from PIL import Image\n", "import torchvision\n", "from torch.utils import data as Data\n", "from torchvision import datasets, transforms\n", "import matplotlib.pyplot as plt\n", "import matplotlib.image as mpimg\n", "import numpy as np\n", "import pandas as pd\n", "import pytesseract as pt\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import MultiLabelBinarizer\n", "\n", "import cv2\n", "\n", "import tensorflow as tf\n", "import plotly.express as px\n", "import matplotlib.pyplot as plt\n", "import xml.etree.ElementTree as xet\n", "\n", "from glob import glob\n", "from skimage import io\n", "from shutil import copy\n", "from tensorflow.keras.models import Model\n", "from tensorflow.keras.callbacks import TensorBoard\n", "from sklearn.model_selection import train_test_split\n", "from tensorflow.keras.applications import InceptionResNetV2\n", "from tensorflow.keras.layers import Dense, Dropout, Flatten, Input\n", "from tensorflow.keras.preprocessing.image import load_img, img_to_array" ] }, { "cell_type": "code", "source": [ "!unzip \"/content/drive/MyDrive/AA/archive.zip\"" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bMWQ2Kv-FWhk", "outputId": "3e8e3857-5e00-4625-8cce-6052b7ca6a52" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Archive: /content/drive/MyDrive/AA/archive.zip\n", " inflating: annotations/Cars0.xml \n", " inflating: annotations/Cars1.xml \n", " inflating: annotations/Cars10.xml \n", " inflating: annotations/Cars100.xml \n", " inflating: annotations/Cars101.xml \n", " inflating: annotations/Cars102.xml \n", " inflating: annotations/Cars103.xml \n", " inflating: annotations/Cars104.xml \n", " inflating: annotations/Cars105.xml \n", " inflating: annotations/Cars106.xml \n", " inflating: annotations/Cars107.xml \n", " inflating: annotations/Cars108.xml \n", " inflating: annotations/Cars109.xml \n", " inflating: annotations/Cars11.xml \n", " inflating: annotations/Cars110.xml \n", " inflating: annotations/Cars111.xml \n", " inflating: annotations/Cars112.xml \n", " inflating: annotations/Cars113.xml \n", " inflating: annotations/Cars114.xml \n", " inflating: annotations/Cars115.xml \n", " inflating: annotations/Cars116.xml \n", " inflating: annotations/Cars117.xml \n", " inflating: annotations/Cars118.xml \n", " inflating: annotations/Cars119.xml \n", " inflating: annotations/Cars12.xml \n", " inflating: annotations/Cars120.xml \n", " inflating: annotations/Cars121.xml \n", " inflating: annotations/Cars122.xml \n", " inflating: annotations/Cars123.xml \n", " inflating: annotations/Cars124.xml \n", " inflating: annotations/Cars125.xml \n", " inflating: annotations/Cars126.xml \n", " inflating: annotations/Cars127.xml \n", " inflating: annotations/Cars128.xml \n", " inflating: annotations/Cars129.xml \n", " inflating: annotations/Cars13.xml \n", " inflating: annotations/Cars130.xml \n", " inflating: annotations/Cars131.xml \n", " inflating: annotations/Cars132.xml \n", " inflating: annotations/Cars133.xml \n", " inflating: annotations/Cars134.xml \n", " inflating: annotations/Cars135.xml \n", " inflating: annotations/Cars136.xml \n", " inflating: annotations/Cars137.xml \n", " inflating: annotations/Cars138.xml \n", " inflating: annotations/Cars139.xml \n", " inflating: annotations/Cars14.xml \n", " inflating: annotations/Cars140.xml \n", " inflating: annotations/Cars141.xml \n", " inflating: annotations/Cars142.xml \n", " inflating: annotations/Cars143.xml \n", " inflating: annotations/Cars144.xml \n", " inflating: annotations/Cars145.xml \n", " inflating: annotations/Cars146.xml \n", " inflating: annotations/Cars147.xml \n", " inflating: annotations/Cars148.xml \n", " inflating: annotations/Cars149.xml \n", " inflating: annotations/Cars15.xml \n", " inflating: annotations/Cars150.xml \n", " inflating: annotations/Cars151.xml \n", " inflating: annotations/Cars152.xml \n", " inflating: annotations/Cars153.xml \n", " inflating: annotations/Cars154.xml \n", " inflating: annotations/Cars155.xml \n", " inflating: annotations/Cars156.xml \n", " inflating: annotations/Cars157.xml \n", " inflating: annotations/Cars158.xml \n", " inflating: annotations/Cars159.xml \n", " inflating: annotations/Cars16.xml \n", " inflating: annotations/Cars160.xml \n", " inflating: annotations/Cars161.xml \n", " inflating: annotations/Cars162.xml \n", " inflating: annotations/Cars163.xml \n", " inflating: annotations/Cars164.xml \n", " inflating: annotations/Cars165.xml \n", " inflating: annotations/Cars166.xml \n", " inflating: annotations/Cars167.xml \n", " inflating: annotations/Cars168.xml \n", " inflating: annotations/Cars169.xml \n", " inflating: annotations/Cars17.xml \n", " inflating: annotations/Cars170.xml \n", " inflating: annotations/Cars171.xml \n", " inflating: annotations/Cars172.xml \n", " inflating: annotations/Cars173.xml \n", " inflating: annotations/Cars174.xml \n", " inflating: annotations/Cars175.xml \n", " inflating: annotations/Cars176.xml \n", " inflating: annotations/Cars177.xml \n", " inflating: annotations/Cars178.xml \n", " inflating: annotations/Cars179.xml \n", " inflating: annotations/Cars18.xml \n", " inflating: annotations/Cars180.xml \n", " inflating: annotations/Cars181.xml \n", " inflating: annotations/Cars182.xml \n", " inflating: annotations/Cars183.xml \n", " inflating: annotations/Cars184.xml \n", " inflating: annotations/Cars185.xml \n", " inflating: annotations/Cars186.xml \n", " inflating: annotations/Cars187.xml \n", " inflating: annotations/Cars188.xml \n", " inflating: annotations/Cars189.xml \n", " inflating: annotations/Cars19.xml \n", " inflating: annotations/Cars190.xml \n", " inflating: annotations/Cars191.xml \n", " inflating: annotations/Cars192.xml \n", " inflating: annotations/Cars193.xml \n", " inflating: annotations/Cars194.xml \n", " inflating: annotations/Cars195.xml \n", " inflating: annotations/Cars196.xml \n", " inflating: annotations/Cars197.xml \n", " inflating: annotations/Cars198.xml \n", " inflating: annotations/Cars199.xml \n", " inflating: annotations/Cars2.xml \n", " inflating: annotations/Cars20.xml \n", " inflating: annotations/Cars200.xml \n", " inflating: annotations/Cars201.xml \n", " inflating: annotations/Cars202.xml \n", " inflating: annotations/Cars203.xml \n", " inflating: annotations/Cars204.xml \n", " inflating: annotations/Cars205.xml \n", " inflating: annotations/Cars206.xml \n", " inflating: annotations/Cars207.xml \n", " inflating: annotations/Cars208.xml \n", " inflating: annotations/Cars209.xml \n", " inflating: annotations/Cars21.xml \n", " inflating: annotations/Cars210.xml \n", " inflating: annotations/Cars211.xml \n", " inflating: annotations/Cars212.xml \n", " inflating: annotations/Cars213.xml \n", " inflating: annotations/Cars214.xml \n", " inflating: annotations/Cars215.xml \n", " inflating: annotations/Cars216.xml \n", " inflating: annotations/Cars217.xml \n", " inflating: annotations/Cars218.xml \n", " inflating: annotations/Cars219.xml \n", " inflating: annotations/Cars22.xml \n", " inflating: annotations/Cars220.xml \n", " inflating: annotations/Cars221.xml \n", " inflating: annotations/Cars222.xml \n", " inflating: annotations/Cars223.xml \n", " inflating: annotations/Cars224.xml \n", " inflating: annotations/Cars225.xml \n", " inflating: annotations/Cars226.xml \n", " inflating: annotations/Cars227.xml \n", " inflating: annotations/Cars228.xml \n", " inflating: annotations/Cars229.xml \n", " inflating: annotations/Cars23.xml \n", " inflating: annotations/Cars230.xml \n", " inflating: annotations/Cars231.xml \n", " inflating: annotations/Cars232.xml \n", " inflating: annotations/Cars233.xml \n", " inflating: annotations/Cars234.xml \n", " inflating: annotations/Cars235.xml \n", " inflating: annotations/Cars236.xml \n", " inflating: annotations/Cars237.xml \n", " inflating: annotations/Cars238.xml \n", " inflating: annotations/Cars239.xml \n", " inflating: annotations/Cars24.xml \n", " inflating: annotations/Cars240.xml \n", " inflating: annotations/Cars241.xml \n", " inflating: annotations/Cars242.xml \n", " inflating: annotations/Cars243.xml \n", " inflating: annotations/Cars244.xml \n", " inflating: annotations/Cars245.xml \n", " inflating: annotations/Cars246.xml \n", " inflating: annotations/Cars247.xml \n", " inflating: annotations/Cars248.xml \n", " inflating: annotations/Cars249.xml \n", " inflating: annotations/Cars25.xml \n", " inflating: annotations/Cars250.xml \n", " inflating: annotations/Cars251.xml \n", " inflating: annotations/Cars252.xml \n", " inflating: annotations/Cars253.xml \n", " inflating: annotations/Cars254.xml \n", " inflating: annotations/Cars255.xml \n", " inflating: annotations/Cars256.xml \n", " inflating: annotations/Cars257.xml \n", " inflating: annotations/Cars258.xml \n", " inflating: annotations/Cars259.xml \n", " inflating: annotations/Cars26.xml \n", " inflating: annotations/Cars260.xml \n", " inflating: annotations/Cars261.xml \n", " inflating: annotations/Cars262.xml \n", " inflating: annotations/Cars263.xml \n", " inflating: annotations/Cars264.xml \n", " inflating: annotations/Cars265.xml \n", " inflating: annotations/Cars266.xml \n", " inflating: annotations/Cars267.xml \n", " inflating: annotations/Cars268.xml \n", " inflating: annotations/Cars269.xml \n", " inflating: annotations/Cars27.xml \n", " inflating: annotations/Cars270.xml \n", " inflating: annotations/Cars271.xml \n", " inflating: annotations/Cars272.xml \n", " inflating: annotations/Cars273.xml \n", " inflating: annotations/Cars274.xml \n", " inflating: annotations/Cars275.xml \n", " inflating: annotations/Cars276.xml \n", " inflating: annotations/Cars277.xml \n", " inflating: annotations/Cars278.xml \n", " inflating: annotations/Cars279.xml \n", " inflating: annotations/Cars28.xml \n", " inflating: annotations/Cars280.xml \n", " inflating: annotations/Cars281.xml \n", " inflating: annotations/Cars282.xml \n", " inflating: annotations/Cars283.xml \n", " inflating: annotations/Cars284.xml \n", " inflating: annotations/Cars285.xml \n", " inflating: annotations/Cars286.xml \n", " inflating: annotations/Cars287.xml \n", " inflating: annotations/Cars288.xml \n", " inflating: annotations/Cars289.xml \n", " inflating: annotations/Cars29.xml \n", " inflating: annotations/Cars290.xml \n", " inflating: annotations/Cars291.xml \n", " inflating: annotations/Cars292.xml \n", " inflating: annotations/Cars293.xml \n", " inflating: annotations/Cars294.xml \n", " inflating: annotations/Cars295.xml \n", " inflating: annotations/Cars296.xml \n", " inflating: annotations/Cars297.xml \n", " inflating: annotations/Cars298.xml \n", " inflating: annotations/Cars299.xml \n", " inflating: annotations/Cars3.xml \n", " inflating: annotations/Cars30.xml \n", " inflating: annotations/Cars300.xml \n", " inflating: annotations/Cars301.xml \n", " inflating: annotations/Cars302.xml \n", " inflating: annotations/Cars303.xml \n", " inflating: annotations/Cars304.xml \n", " inflating: annotations/Cars305.xml \n", " inflating: annotations/Cars306.xml \n", " inflating: annotations/Cars307.xml \n", " inflating: annotations/Cars308.xml \n", " inflating: annotations/Cars309.xml \n", " inflating: annotations/Cars31.xml \n", " inflating: annotations/Cars310.xml \n", " inflating: annotations/Cars311.xml \n", " inflating: annotations/Cars312.xml \n", " inflating: annotations/Cars313.xml \n", " inflating: annotations/Cars314.xml \n", " inflating: annotations/Cars315.xml \n", " inflating: annotations/Cars316.xml \n", " inflating: annotations/Cars317.xml \n", " inflating: annotations/Cars318.xml \n", " inflating: annotations/Cars319.xml \n", " inflating: annotations/Cars32.xml \n", " inflating: annotations/Cars320.xml \n", " inflating: annotations/Cars321.xml \n", " inflating: annotations/Cars322.xml \n", " inflating: annotations/Cars323.xml \n", " inflating: annotations/Cars324.xml \n", " inflating: annotations/Cars325.xml \n", " inflating: annotations/Cars326.xml \n", " inflating: annotations/Cars327.xml \n", " inflating: annotations/Cars328.xml \n", " inflating: annotations/Cars329.xml \n", " inflating: annotations/Cars33.xml \n", " inflating: annotations/Cars330.xml \n", " inflating: annotations/Cars331.xml \n", " inflating: annotations/Cars332.xml \n", " inflating: annotations/Cars333.xml \n", " inflating: annotations/Cars334.xml \n", " inflating: annotations/Cars335.xml \n", " inflating: annotations/Cars336.xml \n", " inflating: annotations/Cars337.xml \n", " inflating: annotations/Cars338.xml \n", " inflating: annotations/Cars339.xml \n", " inflating: annotations/Cars34.xml \n", " inflating: annotations/Cars340.xml \n", " inflating: annotations/Cars341.xml \n", " inflating: annotations/Cars342.xml \n", " inflating: annotations/Cars343.xml \n", " inflating: annotations/Cars344.xml \n", " inflating: annotations/Cars345.xml \n", " inflating: annotations/Cars346.xml \n", " inflating: annotations/Cars347.xml \n", " inflating: annotations/Cars348.xml \n", " inflating: annotations/Cars349.xml \n", " inflating: annotations/Cars35.xml \n", " inflating: annotations/Cars350.xml \n", " inflating: annotations/Cars351.xml \n", " inflating: annotations/Cars352.xml \n", " inflating: annotations/Cars353.xml \n", " inflating: annotations/Cars354.xml \n", " inflating: annotations/Cars355.xml \n", " inflating: annotations/Cars356.xml \n", " inflating: annotations/Cars357.xml \n", " inflating: annotations/Cars358.xml \n", " inflating: annotations/Cars359.xml \n", " inflating: annotations/Cars36.xml \n", " inflating: annotations/Cars360.xml \n", " inflating: annotations/Cars361.xml \n", " inflating: annotations/Cars362.xml \n", " inflating: annotations/Cars363.xml \n", " inflating: annotations/Cars364.xml \n", " inflating: annotations/Cars365.xml \n", " inflating: annotations/Cars366.xml \n", " inflating: annotations/Cars367.xml \n", " inflating: annotations/Cars368.xml \n", " inflating: annotations/Cars369.xml \n", " inflating: annotations/Cars37.xml \n", " inflating: annotations/Cars370.xml \n", " inflating: annotations/Cars371.xml \n", " inflating: annotations/Cars372.xml \n", " inflating: annotations/Cars373.xml \n", " inflating: annotations/Cars374.xml \n", " inflating: annotations/Cars375.xml \n", " inflating: annotations/Cars376.xml \n", " inflating: annotations/Cars377.xml \n", " inflating: annotations/Cars378.xml \n", " inflating: annotations/Cars379.xml \n", " inflating: annotations/Cars38.xml \n", " inflating: annotations/Cars380.xml \n", " inflating: annotations/Cars381.xml \n", " inflating: annotations/Cars382.xml \n", " inflating: annotations/Cars383.xml \n", " inflating: annotations/Cars384.xml \n", " inflating: annotations/Cars385.xml \n", " inflating: annotations/Cars386.xml \n", " inflating: annotations/Cars387.xml \n", " inflating: annotations/Cars388.xml \n", " inflating: annotations/Cars389.xml \n", " inflating: annotations/Cars39.xml \n", " inflating: annotations/Cars390.xml \n", " inflating: annotations/Cars391.xml \n", " inflating: annotations/Cars392.xml \n", " inflating: annotations/Cars393.xml \n", " inflating: annotations/Cars394.xml \n", " inflating: annotations/Cars395.xml \n", " inflating: annotations/Cars396.xml \n", " inflating: annotations/Cars397.xml \n", " inflating: annotations/Cars398.xml \n", " inflating: annotations/Cars399.xml \n", " inflating: annotations/Cars4.xml \n", " inflating: annotations/Cars40.xml \n", " inflating: annotations/Cars400.xml \n", " inflating: annotations/Cars401.xml \n", " inflating: annotations/Cars402.xml \n", " inflating: annotations/Cars403.xml \n", " inflating: annotations/Cars404.xml \n", " inflating: annotations/Cars405.xml \n", " inflating: annotations/Cars406.xml \n", " inflating: annotations/Cars407.xml \n", " inflating: annotations/Cars408.xml \n", " inflating: annotations/Cars409.xml \n", " inflating: annotations/Cars41.xml \n", " inflating: annotations/Cars410.xml \n", " inflating: annotations/Cars411.xml \n", " inflating: annotations/Cars412.xml \n", " inflating: annotations/Cars413.xml \n", " inflating: annotations/Cars414.xml \n", " inflating: annotations/Cars415.xml \n", " inflating: annotations/Cars416.xml \n", " inflating: annotations/Cars417.xml \n", " inflating: annotations/Cars418.xml \n", " inflating: annotations/Cars419.xml \n", " inflating: annotations/Cars42.xml \n", " inflating: annotations/Cars420.xml \n", " inflating: annotations/Cars421.xml \n", " inflating: annotations/Cars422.xml \n", " inflating: annotations/Cars423.xml \n", " inflating: annotations/Cars424.xml \n", " inflating: annotations/Cars425.xml \n", " inflating: annotations/Cars426.xml \n", " inflating: annotations/Cars427.xml \n", " inflating: annotations/Cars428.xml \n", " inflating: annotations/Cars429.xml \n", " inflating: annotations/Cars43.xml \n", " inflating: annotations/Cars430.xml \n", " inflating: annotations/Cars431.xml \n", " inflating: annotations/Cars432.xml \n", " inflating: annotations/Cars44.xml \n", " inflating: annotations/Cars45.xml \n", " inflating: annotations/Cars46.xml \n", " inflating: annotations/Cars47.xml \n", " inflating: annotations/Cars48.xml \n", " inflating: annotations/Cars49.xml \n", " inflating: annotations/Cars5.xml \n", " inflating: annotations/Cars50.xml \n", " inflating: annotations/Cars51.xml \n", " inflating: annotations/Cars52.xml \n", " inflating: annotations/Cars53.xml \n", " inflating: annotations/Cars54.xml \n", " inflating: annotations/Cars55.xml \n", " inflating: annotations/Cars56.xml \n", " inflating: annotations/Cars57.xml \n", " inflating: annotations/Cars58.xml \n", " inflating: annotations/Cars59.xml \n", " inflating: annotations/Cars6.xml \n", " inflating: annotations/Cars60.xml \n", " inflating: annotations/Cars61.xml \n", " inflating: annotations/Cars62.xml \n", " inflating: annotations/Cars63.xml \n", " inflating: annotations/Cars64.xml \n", " inflating: annotations/Cars65.xml \n", " inflating: annotations/Cars66.xml \n", " inflating: annotations/Cars67.xml \n", " inflating: annotations/Cars68.xml \n", " inflating: annotations/Cars69.xml \n", " inflating: annotations/Cars7.xml \n", " inflating: annotations/Cars70.xml \n", " inflating: annotations/Cars71.xml \n", " inflating: annotations/Cars72.xml \n", " inflating: annotations/Cars73.xml \n", " inflating: annotations/Cars74.xml \n", " inflating: annotations/Cars75.xml \n", " inflating: annotations/Cars76.xml \n", " inflating: annotations/Cars77.xml \n", " inflating: annotations/Cars78.xml \n", " inflating: annotations/Cars79.xml \n", " inflating: annotations/Cars8.xml \n", " inflating: annotations/Cars80.xml \n", " inflating: annotations/Cars81.xml \n", " inflating: annotations/Cars82.xml \n", " inflating: annotations/Cars83.xml \n", " inflating: annotations/Cars84.xml \n", " inflating: annotations/Cars85.xml \n", " inflating: annotations/Cars86.xml \n", " inflating: annotations/Cars87.xml \n", " inflating: annotations/Cars88.xml \n", " inflating: annotations/Cars89.xml \n", " inflating: annotations/Cars9.xml \n", " inflating: annotations/Cars90.xml \n", " inflating: annotations/Cars91.xml \n", " inflating: annotations/Cars92.xml \n", " inflating: annotations/Cars93.xml \n", " inflating: annotations/Cars94.xml \n", " inflating: annotations/Cars95.xml \n", " inflating: annotations/Cars96.xml \n", " inflating: annotations/Cars97.xml \n", " inflating: annotations/Cars98.xml \n", " inflating: annotations/Cars99.xml \n", " inflating: images/Cars0.png \n", " inflating: images/Cars1.png \n", " inflating: images/Cars10.png \n", " inflating: images/Cars100.png \n", " inflating: images/Cars101.png \n", " inflating: images/Cars102.png \n", " inflating: images/Cars103.png \n", " inflating: images/Cars104.png \n", " inflating: images/Cars105.png \n", " inflating: images/Cars106.png \n", " inflating: images/Cars107.png \n", " inflating: images/Cars108.png \n", " inflating: images/Cars109.png \n", " inflating: images/Cars11.png \n", " inflating: images/Cars110.png \n", " inflating: images/Cars111.png \n", " inflating: images/Cars112.png \n", " inflating: images/Cars113.png \n", " inflating: images/Cars114.png \n", " inflating: images/Cars115.png \n", " inflating: images/Cars116.png \n", " inflating: images/Cars117.png \n", " inflating: images/Cars118.png \n", " inflating: images/Cars119.png \n", " inflating: images/Cars12.png \n", " inflating: images/Cars120.png \n", " inflating: images/Cars121.png \n", " inflating: images/Cars122.png \n", " inflating: images/Cars123.png \n", " inflating: images/Cars124.png \n", " inflating: images/Cars125.png \n", " inflating: images/Cars126.png \n", " inflating: images/Cars127.png \n", " inflating: images/Cars128.png \n", " inflating: images/Cars129.png \n", " inflating: images/Cars13.png \n", " inflating: images/Cars130.png \n", " inflating: images/Cars131.png \n", " inflating: images/Cars132.png \n", " inflating: images/Cars133.png \n", " inflating: images/Cars134.png \n", " inflating: images/Cars135.png \n", " inflating: images/Cars136.png \n", " inflating: images/Cars137.png \n", " inflating: images/Cars138.png \n", " inflating: images/Cars139.png \n", " inflating: images/Cars14.png \n", " inflating: images/Cars140.png \n", " inflating: images/Cars141.png \n", " inflating: images/Cars142.png \n", " inflating: images/Cars143.png \n", " inflating: images/Cars144.png \n", " inflating: images/Cars145.png \n", " inflating: images/Cars146.png \n", " inflating: images/Cars147.png \n", " inflating: images/Cars148.png \n", " inflating: images/Cars149.png \n", " inflating: images/Cars15.png \n", " inflating: images/Cars150.png \n", " inflating: images/Cars151.png \n", " inflating: images/Cars152.png \n", " inflating: images/Cars153.png \n", " inflating: images/Cars154.png \n", " inflating: images/Cars155.png \n", " inflating: images/Cars156.png \n", " inflating: images/Cars157.png \n", " inflating: images/Cars158.png \n", " inflating: images/Cars159.png \n", " inflating: images/Cars16.png \n", " inflating: images/Cars160.png \n", " inflating: images/Cars161.png \n", " inflating: images/Cars162.png \n", " inflating: images/Cars163.png \n", " inflating: images/Cars164.png \n", " inflating: images/Cars165.png \n", " inflating: images/Cars166.png \n", " inflating: images/Cars167.png \n", " inflating: images/Cars168.png \n", " inflating: images/Cars169.png \n", " inflating: images/Cars17.png \n", " inflating: images/Cars170.png \n", " inflating: images/Cars171.png \n", " inflating: images/Cars172.png \n", " inflating: images/Cars173.png \n", " inflating: images/Cars174.png \n", " inflating: images/Cars175.png \n", " inflating: images/Cars176.png \n", " inflating: images/Cars177.png \n", " inflating: images/Cars178.png \n", " inflating: images/Cars179.png \n", " inflating: images/Cars18.png \n", " inflating: images/Cars180.png \n", " inflating: images/Cars181.png \n", " inflating: images/Cars182.png \n", " inflating: images/Cars183.png \n", " inflating: images/Cars184.png \n", " inflating: images/Cars185.png \n", " inflating: images/Cars186.png \n", " inflating: images/Cars187.png \n", " inflating: images/Cars188.png \n", " inflating: images/Cars189.png \n", " inflating: images/Cars19.png \n", " inflating: images/Cars190.png \n", " inflating: images/Cars191.png \n", " inflating: images/Cars192.png \n", " inflating: images/Cars193.png \n", " inflating: images/Cars194.png \n", " inflating: images/Cars195.png \n", " inflating: images/Cars196.png \n", " inflating: images/Cars197.png \n", " inflating: images/Cars198.png \n", " inflating: images/Cars199.png \n", " inflating: images/Cars2.png \n", " inflating: images/Cars20.png \n", " inflating: images/Cars200.png \n", " inflating: images/Cars201.png \n", " inflating: images/Cars202.png \n", " inflating: images/Cars203.png \n", " inflating: images/Cars204.png \n", " inflating: images/Cars205.png \n", " inflating: images/Cars206.png \n", " inflating: images/Cars207.png \n", " inflating: images/Cars208.png \n", " inflating: images/Cars209.png \n", " inflating: images/Cars21.png \n", " inflating: images/Cars210.png \n", " inflating: images/Cars211.png \n", " inflating: images/Cars212.png \n", " inflating: images/Cars213.png \n", " inflating: images/Cars214.png \n", " inflating: images/Cars215.png \n", " inflating: images/Cars216.png \n", " inflating: images/Cars217.png \n", " inflating: images/Cars218.png \n", " inflating: images/Cars219.png \n", " inflating: images/Cars22.png \n", " inflating: images/Cars220.png \n", " inflating: images/Cars221.png \n", " inflating: images/Cars222.png \n", " inflating: images/Cars223.png \n", " inflating: images/Cars224.png \n", " inflating: images/Cars225.png \n", " inflating: images/Cars226.png \n", " inflating: images/Cars227.png \n", " inflating: images/Cars228.png \n", " inflating: images/Cars229.png \n", " inflating: images/Cars23.png \n", " inflating: images/Cars230.png \n", " inflating: images/Cars231.png \n", " inflating: images/Cars232.png \n", " inflating: images/Cars233.png \n", " inflating: images/Cars234.png \n", " inflating: images/Cars235.png \n", " inflating: images/Cars236.png \n", " inflating: images/Cars237.png \n", " inflating: images/Cars238.png \n", " inflating: images/Cars239.png \n", " inflating: images/Cars24.png \n", " inflating: images/Cars240.png \n", " inflating: images/Cars241.png \n", " inflating: images/Cars242.png \n", " inflating: images/Cars243.png \n", " inflating: images/Cars244.png \n", " inflating: images/Cars245.png \n", " inflating: images/Cars246.png \n", " inflating: images/Cars247.png \n", " inflating: images/Cars248.png \n", " inflating: images/Cars249.png \n", " inflating: images/Cars25.png \n", " inflating: images/Cars250.png \n", " inflating: images/Cars251.png \n", " inflating: images/Cars252.png \n", " inflating: images/Cars253.png \n", " inflating: images/Cars254.png \n", " inflating: images/Cars255.png \n", " inflating: images/Cars256.png \n", " inflating: images/Cars257.png \n", " inflating: images/Cars258.png \n", " inflating: images/Cars259.png \n", " inflating: images/Cars26.png \n", " inflating: images/Cars260.png \n", " inflating: images/Cars261.png \n", " inflating: images/Cars262.png \n", " inflating: images/Cars263.png \n", " inflating: images/Cars264.png \n", " inflating: images/Cars265.png \n", " inflating: images/Cars266.png \n", " inflating: images/Cars267.png \n", " inflating: images/Cars268.png \n", " inflating: images/Cars269.png \n", " inflating: images/Cars27.png \n", " inflating: images/Cars270.png \n", " inflating: images/Cars271.png \n", " inflating: images/Cars272.png \n", " inflating: images/Cars273.png \n", " inflating: images/Cars274.png \n", " inflating: images/Cars275.png \n", " inflating: images/Cars276.png \n", " inflating: images/Cars277.png \n", " inflating: images/Cars278.png \n", " inflating: images/Cars279.png \n", " inflating: images/Cars28.png \n", " inflating: images/Cars280.png \n", " inflating: images/Cars281.png \n", " inflating: images/Cars282.png \n", " inflating: images/Cars283.png \n", " inflating: images/Cars284.png \n", " inflating: images/Cars285.png \n", " inflating: images/Cars286.png \n", " inflating: images/Cars287.png \n", " inflating: images/Cars288.png \n", " inflating: images/Cars289.png \n", " inflating: images/Cars29.png \n", " inflating: images/Cars290.png \n", " inflating: images/Cars291.png \n", " inflating: images/Cars292.png \n", " inflating: images/Cars293.png \n", " inflating: images/Cars294.png \n", " inflating: images/Cars295.png \n", " inflating: images/Cars296.png \n", " inflating: images/Cars297.png \n", " inflating: images/Cars298.png \n", " inflating: images/Cars299.png \n", " inflating: images/Cars3.png \n", " inflating: images/Cars30.png \n", " inflating: images/Cars300.png \n", " inflating: images/Cars301.png \n", " inflating: images/Cars302.png \n", " inflating: images/Cars303.png \n", " inflating: images/Cars304.png \n", " inflating: images/Cars305.png \n", " inflating: images/Cars306.png \n", " inflating: images/Cars307.png \n", " inflating: images/Cars308.png \n", " inflating: images/Cars309.png \n", " inflating: images/Cars31.png \n", " inflating: images/Cars310.png \n", " inflating: images/Cars311.png \n", " inflating: images/Cars312.png \n", " inflating: images/Cars313.png \n", " inflating: images/Cars314.png \n", " inflating: images/Cars315.png \n", " inflating: images/Cars316.png \n", " inflating: images/Cars317.png \n", " inflating: images/Cars318.png \n", " inflating: images/Cars319.png \n", " inflating: images/Cars32.png \n", " inflating: images/Cars320.png \n", " inflating: images/Cars321.png \n", " inflating: images/Cars322.png \n", " inflating: images/Cars323.png \n", " inflating: images/Cars324.png \n", " inflating: images/Cars325.png \n", " inflating: images/Cars326.png \n", " inflating: images/Cars327.png \n", " inflating: images/Cars328.png \n", " inflating: images/Cars329.png \n", " inflating: images/Cars33.png \n", " inflating: images/Cars330.png \n", " inflating: images/Cars331.png \n", " inflating: images/Cars332.png \n", " inflating: images/Cars333.png \n", " inflating: images/Cars334.png \n", " inflating: images/Cars335.png \n", " inflating: images/Cars336.png \n", " inflating: images/Cars337.png \n", " inflating: images/Cars338.png \n", " inflating: images/Cars339.png \n", " inflating: images/Cars34.png \n", " inflating: images/Cars340.png \n", " inflating: images/Cars341.png \n", " inflating: images/Cars342.png \n", " inflating: images/Cars343.png \n", " inflating: images/Cars344.png \n", " inflating: images/Cars345.png \n", " inflating: images/Cars346.png \n", " inflating: images/Cars347.png \n", " inflating: images/Cars348.png \n", " inflating: images/Cars349.png \n", " inflating: images/Cars35.png \n", " inflating: images/Cars350.png \n", " inflating: images/Cars351.png \n", " inflating: images/Cars352.png \n", " inflating: images/Cars353.png \n", " inflating: images/Cars354.png \n", " inflating: images/Cars355.png \n", " inflating: images/Cars356.png \n", " inflating: images/Cars357.png \n", " inflating: images/Cars358.png \n", " inflating: images/Cars359.png \n", " inflating: images/Cars36.png \n", " inflating: images/Cars360.png \n", " inflating: images/Cars361.png \n", " inflating: images/Cars362.png \n", " inflating: images/Cars363.png \n", " inflating: images/Cars364.png \n", " inflating: images/Cars365.png \n", " inflating: images/Cars366.png \n", " inflating: images/Cars367.png \n", " inflating: images/Cars368.png \n", " inflating: images/Cars369.png \n", " inflating: images/Cars37.png \n", " inflating: images/Cars370.png \n", " inflating: images/Cars371.png \n", " inflating: images/Cars372.png \n", " inflating: images/Cars373.png \n", " inflating: images/Cars374.png \n", " inflating: images/Cars375.png \n", " inflating: images/Cars376.png \n", " inflating: images/Cars377.png \n", " inflating: images/Cars378.png \n", " inflating: images/Cars379.png \n", " inflating: images/Cars38.png \n", " inflating: images/Cars380.png \n", " inflating: images/Cars381.png \n", " inflating: images/Cars382.png \n", " inflating: images/Cars383.png \n", " inflating: images/Cars384.png \n", " inflating: images/Cars385.png \n", " inflating: images/Cars386.png \n", " inflating: images/Cars387.png \n", " inflating: images/Cars388.png \n", " inflating: images/Cars389.png \n", " inflating: images/Cars39.png \n", " inflating: images/Cars390.png \n", " inflating: images/Cars391.png \n", " inflating: images/Cars392.png \n", " inflating: images/Cars393.png \n", " inflating: images/Cars394.png \n", " inflating: images/Cars395.png \n", " inflating: images/Cars396.png \n", " inflating: images/Cars397.png \n", " inflating: images/Cars398.png \n", " inflating: images/Cars399.png \n", " inflating: images/Cars4.png \n", " inflating: images/Cars40.png \n", " inflating: images/Cars400.png \n", " inflating: images/Cars401.png \n", " inflating: images/Cars402.png \n", " inflating: images/Cars403.png \n", " inflating: images/Cars404.png \n", " inflating: images/Cars405.png \n", " inflating: images/Cars406.png \n", " inflating: images/Cars407.png \n", " inflating: images/Cars408.png \n", " inflating: images/Cars409.png \n", " inflating: images/Cars41.png \n", " inflating: images/Cars410.png \n", " inflating: images/Cars411.png \n", " inflating: images/Cars412.png \n", " inflating: images/Cars413.png \n", " inflating: images/Cars414.png \n", " inflating: images/Cars415.png \n", " inflating: images/Cars416.png \n", " inflating: images/Cars417.png \n", " inflating: images/Cars418.png \n", " inflating: images/Cars419.png \n", " inflating: images/Cars42.png \n", " inflating: images/Cars420.png \n", " inflating: images/Cars421.png \n", " inflating: images/Cars422.png \n", " inflating: images/Cars423.png \n", " inflating: images/Cars424.png \n", " inflating: images/Cars425.png \n", " inflating: images/Cars426.png \n", " inflating: images/Cars427.png \n", " inflating: images/Cars428.png \n", " inflating: images/Cars429.png \n", " inflating: images/Cars43.png \n", " inflating: images/Cars430.png \n", " inflating: images/Cars431.png \n", " inflating: images/Cars432.png \n", " inflating: images/Cars44.png \n", " inflating: images/Cars45.png \n", " inflating: images/Cars46.png \n", " inflating: images/Cars47.png \n", " inflating: images/Cars48.png \n", " inflating: images/Cars49.png \n", " inflating: images/Cars5.png \n", " inflating: images/Cars50.png \n", " inflating: images/Cars51.png \n", " inflating: images/Cars52.png \n", " inflating: images/Cars53.png \n", " inflating: images/Cars54.png \n", " inflating: images/Cars55.png \n", " inflating: images/Cars56.png \n", " inflating: images/Cars57.png \n", " inflating: images/Cars58.png \n", " inflating: images/Cars59.png \n", " inflating: images/Cars6.png \n", " inflating: images/Cars60.png \n", " inflating: images/Cars61.png \n", " inflating: images/Cars62.png \n", " inflating: images/Cars63.png \n", " inflating: images/Cars64.png \n", " inflating: images/Cars65.png \n", " inflating: images/Cars66.png \n", " inflating: images/Cars67.png \n", " inflating: images/Cars68.png \n", " inflating: images/Cars69.png \n", " inflating: images/Cars7.png \n", " inflating: images/Cars70.png \n", " inflating: images/Cars71.png \n", " inflating: images/Cars72.png \n", " inflating: images/Cars73.png \n", " inflating: images/Cars74.png \n", " inflating: images/Cars75.png \n", " inflating: images/Cars76.png \n", " inflating: images/Cars77.png \n", " inflating: images/Cars78.png \n", " inflating: images/Cars79.png \n", " inflating: images/Cars8.png \n", " inflating: images/Cars80.png \n", " inflating: images/Cars81.png \n", " inflating: images/Cars82.png \n", " inflating: images/Cars83.png \n", " inflating: images/Cars84.png \n", " inflating: images/Cars85.png \n", " inflating: images/Cars86.png \n", " inflating: images/Cars87.png \n", " inflating: images/Cars88.png \n", " inflating: images/Cars89.png \n", " inflating: images/Cars9.png \n", " inflating: images/Cars90.png \n", " inflating: images/Cars91.png \n", " inflating: images/Cars92.png \n", " inflating: images/Cars93.png \n", " inflating: images/Cars94.png \n", " inflating: images/Cars95.png \n", " inflating: images/Cars96.png \n", " inflating: images/Cars97.png \n", " inflating: images/Cars98.png \n", " inflating: images/Cars99.png \n" ] } ] }, { "cell_type": "code", "source": [ "path = glob('/content/annotations/*.xml')\n", "\n", "labels_dict = dict(filepath=[],xmin=[],xmax=[],ymin=[],ymax=[])\n", "for filename in path:\n", "\n", " info = xet.parse(filename)\n", " root = info.getroot()\n", " member_object = root.find('object')\n", " labels_info = member_object.find('bndbox')\n", " xmin = int(labels_info.find('xmin').text)\n", " xmax = int(labels_info.find('xmax').text)\n", " ymin = int(labels_info.find('ymin').text)\n", " ymax = int(labels_info.find('ymax').text)\n", "\n", " labels_dict['filepath'].append(filename)\n", " labels_dict['xmin'].append(xmin)\n", " labels_dict['xmax'].append(xmax)\n", " labels_dict['ymin'].append(ymin)\n", " labels_dict['ymax'].append(ymax)\n", "\n", "labels_dict" ], "metadata": { "id": "kZtKejgnGQjr", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "325847b2-c4a4-4205-b4e6-54b5d13a6d97" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'filepath': ['/content/annotations/Cars420.xml',\n", " '/content/annotations/Cars380.xml',\n", " '/content/annotations/Cars232.xml',\n", " '/content/annotations/Cars89.xml',\n", " '/content/annotations/Cars221.xml',\n", " '/content/annotations/Cars322.xml',\n", " '/content/annotations/Cars243.xml',\n", " '/content/annotations/Cars327.xml',\n", " '/content/annotations/Cars203.xml',\n", " '/content/annotations/Cars422.xml',\n", " '/content/annotations/Cars270.xml',\n", " '/content/annotations/Cars72.xml',\n", " '/content/annotations/Cars346.xml',\n", " '/content/annotations/Cars16.xml',\n", " '/content/annotations/Cars152.xml',\n", " '/content/annotations/Cars83.xml',\n", " '/content/annotations/Cars372.xml',\n", " '/content/annotations/Cars316.xml',\n", " '/content/annotations/Cars32.xml',\n", " '/content/annotations/Cars251.xml',\n", " '/content/annotations/Cars199.xml',\n", " '/content/annotations/Cars392.xml',\n", " '/content/annotations/Cars288.xml',\n", " '/content/annotations/Cars60.xml',\n", " '/content/annotations/Cars135.xml',\n", " '/content/annotations/Cars47.xml',\n", " '/content/annotations/Cars282.xml',\n", " '/content/annotations/Cars10.xml',\n", " '/content/annotations/Cars334.xml',\n", " '/content/annotations/Cars388.xml',\n", " '/content/annotations/Cars137.xml',\n", " '/content/annotations/Cars98.xml',\n", " '/content/annotations/Cars409.xml',\n", " '/content/annotations/Cars341.xml',\n", " '/content/annotations/Cars318.xml',\n", " '/content/annotations/Cars353.xml',\n", " '/content/annotations/Cars139.xml',\n", " '/content/annotations/Cars363.xml',\n", " '/content/annotations/Cars127.xml',\n", " '/content/annotations/Cars27.xml',\n", " '/content/annotations/Cars111.xml',\n", " '/content/annotations/Cars410.xml',\n", " '/content/annotations/Cars389.xml',\n", " '/content/annotations/Cars74.xml',\n", " '/content/annotations/Cars54.xml',\n", " '/content/annotations/Cars209.xml',\n", " '/content/annotations/Cars279.xml',\n", " '/content/annotations/Cars150.xml',\n", " '/content/annotations/Cars184.xml',\n", " '/content/annotations/Cars109.xml',\n", " '/content/annotations/Cars52.xml',\n", " '/content/annotations/Cars403.xml',\n", " '/content/annotations/Cars393.xml',\n", " '/content/annotations/Cars413.xml',\n", " '/content/annotations/Cars41.xml',\n", " '/content/annotations/Cars319.xml',\n", " '/content/annotations/Cars147.xml',\n", " '/content/annotations/Cars383.xml',\n", " '/content/annotations/Cars299.xml',\n", " '/content/annotations/Cars59.xml',\n", " '/content/annotations/Cars407.xml',\n", " '/content/annotations/Cars238.xml',\n", " '/content/annotations/Cars428.xml',\n", " '/content/annotations/Cars313.xml',\n", " '/content/annotations/Cars310.xml',\n", " '/content/annotations/Cars367.xml',\n", " '/content/annotations/Cars26.xml',\n", " '/content/annotations/Cars359.xml',\n", " '/content/annotations/Cars71.xml',\n", " '/content/annotations/Cars157.xml',\n", " '/content/annotations/Cars95.xml',\n", " '/content/annotations/Cars305.xml',\n", " '/content/annotations/Cars354.xml',\n", " '/content/annotations/Cars148.xml',\n", " '/content/annotations/Cars3.xml',\n", " '/content/annotations/Cars28.xml',\n", " '/content/annotations/Cars185.xml',\n", " '/content/annotations/Cars145.xml',\n", " '/content/annotations/Cars85.xml',\n", " '/content/annotations/Cars94.xml',\n", " '/content/annotations/Cars119.xml',\n", " '/content/annotations/Cars240.xml',\n", " '/content/annotations/Cars286.xml',\n", " '/content/annotations/Cars106.xml',\n", " '/content/annotations/Cars314.xml',\n", " '/content/annotations/Cars278.xml',\n", " '/content/annotations/Cars125.xml',\n", " '/content/annotations/Cars390.xml',\n", " '/content/annotations/Cars196.xml',\n", " '/content/annotations/Cars226.xml',\n", " '/content/annotations/Cars217.xml',\n", " '/content/annotations/Cars181.xml',\n", " '/content/annotations/Cars366.xml',\n", " '/content/annotations/Cars40.xml',\n", " '/content/annotations/Cars14.xml',\n", " '/content/annotations/Cars246.xml',\n", " '/content/annotations/Cars301.xml',\n", " '/content/annotations/Cars15.xml',\n", " '/content/annotations/Cars416.xml',\n", " '/content/annotations/Cars17.xml',\n", " '/content/annotations/Cars9.xml',\n", " '/content/annotations/Cars12.xml',\n", " '/content/annotations/Cars432.xml',\n", " '/content/annotations/Cars173.xml',\n", " '/content/annotations/Cars391.xml',\n", " '/content/annotations/Cars317.xml',\n", " '/content/annotations/Cars400.xml',\n", " '/content/annotations/Cars67.xml',\n", " '/content/annotations/Cars357.xml',\n", " '/content/annotations/Cars404.xml',\n", " '/content/annotations/Cars84.xml',\n", " '/content/annotations/Cars202.xml',\n", " '/content/annotations/Cars42.xml',\n", " '/content/annotations/Cars237.xml',\n", " '/content/annotations/Cars262.xml',\n", " '/content/annotations/Cars312.xml',\n", " '/content/annotations/Cars104.xml',\n", " '/content/annotations/Cars123.xml',\n", " '/content/annotations/Cars408.xml',\n", " '/content/annotations/Cars86.xml',\n", " '/content/annotations/Cars228.xml',\n", " '/content/annotations/Cars204.xml',\n", " '/content/annotations/Cars247.xml',\n", " '/content/annotations/Cars371.xml',\n", " '/content/annotations/Cars188.xml',\n", " '/content/annotations/Cars186.xml',\n", " '/content/annotations/Cars218.xml',\n", " '/content/annotations/Cars97.xml',\n", " '/content/annotations/Cars183.xml',\n", " '/content/annotations/Cars117.xml',\n", " '/content/annotations/Cars177.xml',\n", " '/content/annotations/Cars250.xml',\n", " '/content/annotations/Cars142.xml',\n", " '/content/annotations/Cars206.xml',\n", " '/content/annotations/Cars193.xml',\n", " '/content/annotations/Cars311.xml',\n", " '/content/annotations/Cars370.xml',\n", " '/content/annotations/Cars208.xml',\n", " '/content/annotations/Cars129.xml',\n", " '/content/annotations/Cars182.xml',\n", " '/content/annotations/Cars198.xml',\n", " '/content/annotations/Cars337.xml',\n", " '/content/annotations/Cars88.xml',\n", " '/content/annotations/Cars293.xml',\n", " '/content/annotations/Cars159.xml',\n", " '/content/annotations/Cars377.xml',\n", " '/content/annotations/Cars429.xml',\n", " '/content/annotations/Cars365.xml',\n", " '/content/annotations/Cars57.xml',\n", " '/content/annotations/Cars65.xml',\n", " '/content/annotations/Cars280.xml',\n", " '/content/annotations/Cars13.xml',\n", " '/content/annotations/Cars374.xml',\n", " '/content/annotations/Cars378.xml',\n", " '/content/annotations/Cars118.xml',\n", " '/content/annotations/Cars48.xml',\n", " '/content/annotations/Cars35.xml',\n", " '/content/annotations/Cars101.xml',\n", " '/content/annotations/Cars308.xml',\n", " '/content/annotations/Cars138.xml',\n", " '/content/annotations/Cars205.xml',\n", " '/content/annotations/Cars426.xml',\n", " '/content/annotations/Cars344.xml',\n", " '/content/annotations/Cars381.xml',\n", " '/content/annotations/Cars248.xml',\n", " '/content/annotations/Cars231.xml',\n", " '/content/annotations/Cars340.xml',\n", " '/content/annotations/Cars216.xml',\n", " '/content/annotations/Cars417.xml',\n", " '/content/annotations/Cars396.xml',\n", " '/content/annotations/Cars143.xml',\n", " '/content/annotations/Cars213.xml',\n", " '/content/annotations/Cars178.xml',\n", " '/content/annotations/Cars379.xml',\n", " '/content/annotations/Cars162.xml',\n", " '/content/annotations/Cars169.xml',\n", " '/content/annotations/Cars55.xml',\n", " '/content/annotations/Cars343.xml',\n", " '/content/annotations/Cars290.xml',\n", " '/content/annotations/Cars149.xml',\n", " '/content/annotations/Cars140.xml',\n", " '/content/annotations/Cars271.xml',\n", " '/content/annotations/Cars234.xml',\n", " '/content/annotations/Cars160.xml',\n", " '/content/annotations/Cars93.xml',\n", " '/content/annotations/Cars424.xml',\n", " '/content/annotations/Cars114.xml',\n", " '/content/annotations/Cars254.xml',\n", " '/content/annotations/Cars38.xml',\n", " '/content/annotations/Cars294.xml',\n", " '/content/annotations/Cars265.xml',\n", " '/content/annotations/Cars352.xml',\n", " '/content/annotations/Cars387.xml',\n", " '/content/annotations/Cars260.xml',\n", " '/content/annotations/Cars274.xml',\n", " '/content/annotations/Cars4.xml',\n", " '/content/annotations/Cars415.xml',\n", " '/content/annotations/Cars153.xml',\n", " '/content/annotations/Cars368.xml',\n", " '/content/annotations/Cars255.xml',\n", " '/content/annotations/Cars325.xml',\n", " '/content/annotations/Cars302.xml',\n", " '/content/annotations/Cars19.xml',\n", " '/content/annotations/Cars411.xml',\n", " '/content/annotations/Cars350.xml',\n", " '/content/annotations/Cars20.xml',\n", " '/content/annotations/Cars241.xml',\n", " '/content/annotations/Cars44.xml',\n", " '/content/annotations/Cars315.xml',\n", " '/content/annotations/Cars239.xml',\n", " '/content/annotations/Cars168.xml',\n", " '/content/annotations/Cars180.xml',\n", " '/content/annotations/Cars61.xml',\n", " '/content/annotations/Cars328.xml',\n", " '/content/annotations/Cars53.xml',\n", " '/content/annotations/Cars298.xml',\n", " '/content/annotations/Cars46.xml',\n", " '/content/annotations/Cars348.xml',\n", " '/content/annotations/Cars116.xml',\n", " '/content/annotations/Cars266.xml',\n", " '/content/annotations/Cars355.xml',\n", " '/content/annotations/Cars100.xml',\n", " '/content/annotations/Cars36.xml',\n", " '/content/annotations/Cars174.xml',\n", " '/content/annotations/Cars249.xml',\n", " '/content/annotations/Cars303.xml',\n", " '/content/annotations/Cars115.xml',\n", " '/content/annotations/Cars264.xml',\n", " '/content/annotations/Cars0.xml',\n", " '/content/annotations/Cars324.xml',\n", " '/content/annotations/Cars70.xml',\n", " '/content/annotations/Cars398.xml',\n", " '/content/annotations/Cars309.xml',\n", " '/content/annotations/Cars356.xml',\n", " '/content/annotations/Cars399.xml',\n", " '/content/annotations/Cars223.xml',\n", " '/content/annotations/Cars375.xml',\n", " '/content/annotations/Cars34.xml',\n", " '/content/annotations/Cars275.xml',\n", " '/content/annotations/Cars268.xml',\n", " '/content/annotations/Cars382.xml',\n", " '/content/annotations/Cars146.xml',\n", " '/content/annotations/Cars8.xml',\n", " '/content/annotations/Cars175.xml',\n", " '/content/annotations/Cars96.xml',\n", " '/content/annotations/Cars128.xml',\n", " '/content/annotations/Cars126.xml',\n", " '/content/annotations/Cars273.xml',\n", " '/content/annotations/Cars87.xml',\n", " '/content/annotations/Cars110.xml',\n", " '/content/annotations/Cars430.xml',\n", " '/content/annotations/Cars281.xml',\n", " '/content/annotations/Cars297.xml',\n", " '/content/annotations/Cars431.xml',\n", " '/content/annotations/Cars155.xml',\n", " '/content/annotations/Cars166.xml',\n", " '/content/annotations/Cars197.xml',\n", " '/content/annotations/Cars361.xml',\n", " '/content/annotations/Cars92.xml',\n", " '/content/annotations/Cars107.xml',\n", " '/content/annotations/Cars23.xml',\n", " '/content/annotations/Cars62.xml',\n", " '/content/annotations/Cars192.xml',\n", " '/content/annotations/Cars263.xml',\n", " '/content/annotations/Cars300.xml',\n", " '/content/annotations/Cars338.xml',\n", " '/content/annotations/Cars68.xml',\n", " '/content/annotations/Cars78.xml',\n", " '/content/annotations/Cars161.xml',\n", " '/content/annotations/Cars421.xml',\n", " '/content/annotations/Cars358.xml',\n", " '/content/annotations/Cars245.xml',\n", " '/content/annotations/Cars215.xml',\n", " '/content/annotations/Cars45.xml',\n", " '/content/annotations/Cars141.xml',\n", " '/content/annotations/Cars50.xml',\n", " '/content/annotations/Cars6.xml',\n", " '/content/annotations/Cars22.xml',\n", " '/content/annotations/Cars33.xml',\n", " '/content/annotations/Cars58.xml',\n", " '/content/annotations/Cars5.xml',\n", " '/content/annotations/Cars362.xml',\n", " '/content/annotations/Cars201.xml',\n", " '/content/annotations/Cars1.xml',\n", " '/content/annotations/Cars29.xml',\n", " '/content/annotations/Cars351.xml',\n", " '/content/annotations/Cars394.xml',\n", " '/content/annotations/Cars261.xml',\n", " '/content/annotations/Cars423.xml',\n", " '/content/annotations/Cars171.xml',\n", " '/content/annotations/Cars75.xml',\n", " '/content/annotations/Cars307.xml',\n", " '/content/annotations/Cars291.xml',\n", " '/content/annotations/Cars133.xml',\n", " '/content/annotations/Cars406.xml',\n", " '/content/annotations/Cars124.xml',\n", " '/content/annotations/Cars131.xml',\n", " '/content/annotations/Cars212.xml',\n", " '/content/annotations/Cars220.xml',\n", " '/content/annotations/Cars56.xml',\n", " '/content/annotations/Cars258.xml',\n", " '/content/annotations/Cars167.xml',\n", " '/content/annotations/Cars151.xml',\n", " '/content/annotations/Cars339.xml',\n", " '/content/annotations/Cars144.xml',\n", " '/content/annotations/Cars225.xml',\n", " '/content/annotations/Cars412.xml',\n", " '/content/annotations/Cars49.xml',\n", " '/content/annotations/Cars276.xml',\n", " '/content/annotations/Cars418.xml',\n", " '/content/annotations/Cars230.xml',\n", " '/content/annotations/Cars200.xml',\n", " '/content/annotations/Cars30.xml',\n", " '/content/annotations/Cars11.xml',\n", " '/content/annotations/Cars414.xml',\n", " '/content/annotations/Cars99.xml',\n", " '/content/annotations/Cars81.xml',\n", " '/content/annotations/Cars165.xml',\n", " '/content/annotations/Cars112.xml',\n", " '/content/annotations/Cars164.xml',\n", " '/content/annotations/Cars323.xml',\n", " '/content/annotations/Cars269.xml',\n", " '/content/annotations/Cars73.xml',\n", " '/content/annotations/Cars91.xml',\n", " '/content/annotations/Cars284.xml',\n", " '/content/annotations/Cars51.xml',\n", " '/content/annotations/Cars120.xml',\n", " '/content/annotations/Cars224.xml',\n", " '/content/annotations/Cars385.xml',\n", " '/content/annotations/Cars214.xml',\n", " '/content/annotations/Cars195.xml',\n", " '/content/annotations/Cars80.xml',\n", " '/content/annotations/Cars79.xml',\n", " '/content/annotations/Cars113.xml',\n", " '/content/annotations/Cars103.xml',\n", " '/content/annotations/Cars219.xml',\n", " '/content/annotations/Cars64.xml',\n", " '/content/annotations/Cars37.xml',\n", " '/content/annotations/Cars345.xml',\n", " '/content/annotations/Cars364.xml',\n", " '/content/annotations/Cars349.xml',\n", " '/content/annotations/Cars295.xml',\n", " '/content/annotations/Cars21.xml',\n", " '/content/annotations/Cars207.xml',\n", " '/content/annotations/Cars24.xml',\n", " '/content/annotations/Cars267.xml',\n", " '/content/annotations/Cars427.xml',\n", " '/content/annotations/Cars105.xml',\n", " '/content/annotations/Cars257.xml',\n", " '/content/annotations/Cars187.xml',\n", " '/content/annotations/Cars242.xml',\n", " '/content/annotations/Cars306.xml',\n", " '/content/annotations/Cars122.xml',\n", " '/content/annotations/Cars402.xml',\n", " '/content/annotations/Cars76.xml',\n", " '/content/annotations/Cars336.xml',\n", " '/content/annotations/Cars66.xml',\n", " '/content/annotations/Cars132.xml',\n", " '/content/annotations/Cars335.xml',\n", " '/content/annotations/Cars360.xml',\n", " '/content/annotations/Cars77.xml',\n", " '/content/annotations/Cars331.xml',\n", " '/content/annotations/Cars227.xml',\n", " '/content/annotations/Cars256.xml',\n", " '/content/annotations/Cars401.xml',\n", " '/content/annotations/Cars419.xml',\n", " '/content/annotations/Cars304.xml',\n", " '/content/annotations/Cars229.xml',\n", " '/content/annotations/Cars287.xml',\n", " '/content/annotations/Cars329.xml',\n", " '/content/annotations/Cars395.xml',\n", " '/content/annotations/Cars283.xml',\n", " '/content/annotations/Cars347.xml',\n", " '/content/annotations/Cars63.xml',\n", " '/content/annotations/Cars289.xml',\n", " '/content/annotations/Cars25.xml',\n", " '/content/annotations/Cars373.xml',\n", " '/content/annotations/Cars134.xml',\n", " '/content/annotations/Cars235.xml',\n", " '/content/annotations/Cars136.xml',\n", " '/content/annotations/Cars272.xml',\n", " '/content/annotations/Cars156.xml',\n", " '/content/annotations/Cars397.xml',\n", " '/content/annotations/Cars190.xml',\n", " '/content/annotations/Cars330.xml',\n", " '/content/annotations/Cars163.xml',\n", " '/content/annotations/Cars2.xml',\n", " '/content/annotations/Cars320.xml',\n", " '/content/annotations/Cars43.xml',\n", " '/content/annotations/Cars326.xml',\n", " '/content/annotations/Cars342.xml',\n", " '/content/annotations/Cars210.xml',\n", " '/content/annotations/Cars259.xml',\n", " '/content/annotations/Cars369.xml',\n", " '/content/annotations/Cars130.xml',\n", " '/content/annotations/Cars386.xml',\n", " '/content/annotations/Cars172.xml',\n", " '/content/annotations/Cars244.xml',\n", " '/content/annotations/Cars82.xml',\n", " '/content/annotations/Cars285.xml',\n", " '/content/annotations/Cars252.xml',\n", " '/content/annotations/Cars102.xml',\n", " '/content/annotations/Cars7.xml',\n", " '/content/annotations/Cars292.xml',\n", " '/content/annotations/Cars18.xml',\n", " '/content/annotations/Cars277.xml',\n", " '/content/annotations/Cars222.xml',\n", " '/content/annotations/Cars179.xml',\n", " '/content/annotations/Cars158.xml',\n", " '/content/annotations/Cars211.xml',\n", " '/content/annotations/Cars191.xml',\n", " '/content/annotations/Cars233.xml',\n", " '/content/annotations/Cars384.xml',\n", " '/content/annotations/Cars333.xml',\n", " '/content/annotations/Cars253.xml',\n", " '/content/annotations/Cars154.xml',\n", " '/content/annotations/Cars121.xml',\n", " '/content/annotations/Cars296.xml',\n", " '/content/annotations/Cars108.xml',\n", " '/content/annotations/Cars189.xml',\n", " '/content/annotations/Cars405.xml',\n", " '/content/annotations/Cars39.xml',\n", " '/content/annotations/Cars425.xml',\n", " '/content/annotations/Cars170.xml',\n", " '/content/annotations/Cars176.xml',\n", " '/content/annotations/Cars332.xml',\n", " '/content/annotations/Cars321.xml',\n", " '/content/annotations/Cars69.xml',\n", " '/content/annotations/Cars31.xml',\n", " '/content/annotations/Cars236.xml',\n", " '/content/annotations/Cars194.xml',\n", " '/content/annotations/Cars90.xml',\n", " '/content/annotations/Cars376.xml'],\n", " 'xmin': [112,\n", " 316,\n", " 175,\n", " 307,\n", " 102,\n", " 274,\n", " 129,\n", " 138,\n", " 116,\n", " 159,\n", " 75,\n", " 156,\n", " 91,\n", " 36,\n", " 92,\n", " 251,\n", " 221,\n", " 377,\n", " 200,\n", " 136,\n", " 59,\n", " 144,\n", " 97,\n", " 45,\n", " 260,\n", " 6,\n", " 37,\n", " 140,\n", " 155,\n", " 149,\n", " 178,\n", " 85,\n", " 116,\n", " 47,\n", " 119,\n", " 93,\n", " 315,\n", " 153,\n", " 360,\n", " 148,\n", " 319,\n", " 51,\n", " 77,\n", " 115,\n", " 148,\n", " 359,\n", " 42,\n", " 122,\n", " 163,\n", " 115,\n", " 226,\n", " 296,\n", " 94,\n", " 237,\n", " 109,\n", " 167,\n", " 226,\n", " 54,\n", " 101,\n", " 324,\n", " 311,\n", " 276,\n", " 142,\n", " 176,\n", " 241,\n", " 224,\n", " 258,\n", " 228,\n", " 254,\n", " 330,\n", " 278,\n", " 361,\n", " 65,\n", " 135,\n", " 142,\n", " 541,\n", " 154,\n", " 282,\n", " 86,\n", " 176,\n", " 187,\n", " 171,\n", " 201,\n", " 138,\n", " 195,\n", " 278,\n", " 280,\n", " 360,\n", " 105,\n", " 249,\n", " 149,\n", " 69,\n", " 144,\n", " 261,\n", " 94,\n", " 49,\n", " 205,\n", " 310,\n", " 150,\n", " 306,\n", " 164,\n", " 122,\n", " 95,\n", " 170,\n", " 270,\n", " 164,\n", " 303,\n", " 189,\n", " 92,\n", " 162,\n", " 167,\n", " 197,\n", " 177,\n", " 118,\n", " 243,\n", " 111,\n", " 195,\n", " 159,\n", " 160,\n", " 332,\n", " 86,\n", " 193,\n", " 147,\n", " 313,\n", " 32,\n", " 175,\n", " 0,\n", " 98,\n", " 97,\n", " 42,\n", " 149,\n", " 132,\n", " 178,\n", " 95,\n", " 306,\n", " 295,\n", " 285,\n", " 142,\n", " 204,\n", " 168,\n", " 166,\n", " 119,\n", " 168,\n", " 64,\n", " 149,\n", " 177,\n", " 86,\n", " 293,\n", " 270,\n", " 207,\n", " 191,\n", " 191,\n", " 354,\n", " 118,\n", " 193,\n", " 95,\n", " 91,\n", " 167,\n", " 159,\n", " 153,\n", " 156,\n", " 148,\n", " 240,\n", " 65,\n", " 184,\n", " 64,\n", " 270,\n", " 205,\n", " 90,\n", " 181,\n", " 93,\n", " 150,\n", " 126,\n", " 197,\n", " 201,\n", " 114,\n", " 143,\n", " 110,\n", " 224,\n", " 212,\n", " 283,\n", " 262,\n", " 160,\n", " 209,\n", " 347,\n", " 128,\n", " 7,\n", " 166,\n", " 168,\n", " 142,\n", " 87,\n", " 201,\n", " 163,\n", " 170,\n", " 196,\n", " 156,\n", " 133,\n", " 146,\n", " 173,\n", " 116,\n", " 230,\n", " 222,\n", " 148,\n", " 160,\n", " 162,\n", " 18,\n", " 126,\n", " 18,\n", " 12,\n", " 176,\n", " 82,\n", " 151,\n", " 71,\n", " 156,\n", " 146,\n", " 166,\n", " 189,\n", " 156,\n", " 149,\n", " 11,\n", " 164,\n", " 175,\n", " 203,\n", " 283,\n", " 118,\n", " 150,\n", " 315,\n", " 222,\n", " 226,\n", " 151,\n", " 179,\n", " 72,\n", " 152,\n", " 241,\n", " 106,\n", " 347,\n", " 144,\n", " 147,\n", " 99,\n", " 213,\n", " 149,\n", " 143,\n", " 204,\n", " 295,\n", " 133,\n", " 112,\n", " 103,\n", " 74,\n", " 3,\n", " 286,\n", " 38,\n", " 151,\n", " 158,\n", " 55,\n", " 150,\n", " 148,\n", " 222,\n", " 148,\n", " 281,\n", " 141,\n", " 143,\n", " 156,\n", " 108,\n", " 129,\n", " 259,\n", " 297,\n", " 189,\n", " 149,\n", " 312,\n", " 95,\n", " 44,\n", " 91,\n", " 92,\n", " 204,\n", " 266,\n", " 116,\n", " 112,\n", " 207,\n", " 94,\n", " 154,\n", " 226,\n", " 139,\n", " 216,\n", " 134,\n", " 143,\n", " 249,\n", " 291,\n", " 1,\n", " 162,\n", " 192,\n", " 209,\n", " 172,\n", " 71,\n", " 247,\n", " 285,\n", " 305,\n", " 19,\n", " 105,\n", " 142,\n", " 87,\n", " 158,\n", " 176,\n", " 54,\n", " 209,\n", " 101,\n", " 189,\n", " 221,\n", " 268,\n", " 115,\n", " 99,\n", " 222,\n", " 95,\n", " 155,\n", " 131,\n", " 269,\n", " 70,\n", " 297,\n", " 209,\n", " 49,\n", " 160,\n", " 123,\n", " 110,\n", " 119,\n", " 269,\n", " 70,\n", " 264,\n", " 159,\n", " 181,\n", " 69,\n", " 188,\n", " 450,\n", " 94,\n", " 238,\n", " 151,\n", " 230,\n", " 127,\n", " 120,\n", " 146,\n", " 173,\n", " 61,\n", " 38,\n", " 52,\n", " 191,\n", " 254,\n", " 167,\n", " 274,\n", " 236,\n", " 152,\n", " 167,\n", " 96,\n", " 224,\n", " 139,\n", " 195,\n", " 360,\n", " 6,\n", " 122,\n", " 144,\n", " 23,\n", " 156,\n", " 52,\n", " 229,\n", " 164,\n", " 118,\n", " 286,\n", " 158,\n", " 195,\n", " 143,\n", " 253,\n", " 148,\n", " 166,\n", " 241,\n", " 51,\n", " 227,\n", " 54,\n", " 163,\n", " 177,\n", " 161,\n", " 76,\n", " 152,\n", " 164,\n", " 170,\n", " 114,\n", " 144,\n", " 149,\n", " 29,\n", " 220,\n", " 229,\n", " 99,\n", " 142,\n", " 141,\n", " 196,\n", " 54,\n", " 324,\n", " 176,\n", " 91,\n", " 83,\n", " 173,\n", " 20,\n", " 190,\n", " 91,\n", " 174,\n", " 66,\n", " 166,\n", " 91,\n", " 313,\n", " 48,\n", " 178,\n", " 72,\n", " 97,\n", " 28,\n", " 96,\n", " 189,\n", " 219,\n", " 117,\n", " 25,\n", " 70,\n", " 336,\n", " 163,\n", " 158,\n", " 285,\n", " 241,\n", " 225,\n", " 211,\n", " 318,\n", " 72,\n", " 143,\n", " 134,\n", " 77,\n", " 286,\n", " 223,\n", " 267,\n", " 77,\n", " 159],\n", " 'xmax': [160,\n", " 364,\n", " 228,\n", " 345,\n", " 125,\n", " 350,\n", " 465,\n", " 262,\n", " 271,\n", " 296,\n", " 125,\n", " 255,\n", " 197,\n", " 62,\n", " 199,\n", " 326,\n", " 353,\n", " 476,\n", " 251,\n", " 210,\n", " 310,\n", " 260,\n", " 164,\n", " 364,\n", " 319,\n", " 48,\n", " 319,\n", " 303,\n", " 211,\n", " 255,\n", " 204,\n", " 247,\n", " 203,\n", " 115,\n", " 263,\n", " 201,\n", " 337,\n", " 254,\n", " 383,\n", " 245,\n", " 478,\n", " 219,\n", " 222,\n", " 277,\n", " 257,\n", " 387,\n", " 202,\n", " 262,\n", " 212,\n", " 278,\n", " 327,\n", " 332,\n", " 211,\n", " 271,\n", " 159,\n", " 232,\n", " 420,\n", " 88,\n", " 270,\n", " 375,\n", " 346,\n", " 396,\n", " 258,\n", " 252,\n", " 348,\n", " 382,\n", " 321,\n", " 266,\n", " 284,\n", " 366,\n", " 407,\n", " 434,\n", " 102,\n", " 218,\n", " 261,\n", " 598,\n", " 293,\n", " 422,\n", " 146,\n", " 251,\n", " 217,\n", " 232,\n", " 288,\n", " 177,\n", " 274,\n", " 315,\n", " 355,\n", " 397,\n", " 130,\n", " 318,\n", " 223,\n", " 128,\n", " 316,\n", " 317,\n", " 274,\n", " 112,\n", " 303,\n", " 372,\n", " 234,\n", " 351,\n", " 272,\n", " 277,\n", " 196,\n", " 252,\n", " 320,\n", " 240,\n", " 340,\n", " 328,\n", " 137,\n", " 235,\n", " 238,\n", " 249,\n", " 217,\n", " 154,\n", " 285,\n", " 518,\n", " 244,\n", " 237,\n", " 187,\n", " 447,\n", " 113,\n", " 279,\n", " 300,\n", " 371,\n", " 43,\n", " 242,\n", " 27,\n", " 297,\n", " 234,\n", " 86,\n", " 272,\n", " 261,\n", " 273,\n", " 164,\n", " 344,\n", " 355,\n", " 425,\n", " 232,\n", " 282,\n", " 247,\n", " 273,\n", " 136,\n", " 242,\n", " 130,\n", " 261,\n", " 216,\n", " 208,\n", " 373,\n", " 321,\n", " 300,\n", " 213,\n", " 242,\n", " 441,\n", " 254,\n", " 394,\n", " 286,\n", " 137,\n", " 240,\n", " 237,\n", " 254,\n", " 221,\n", " 282,\n", " 379,\n", " 147,\n", " 311,\n", " 133,\n", " 321,\n", " 280,\n", " 273,\n", " 269,\n", " 130,\n", " 233,\n", " 173,\n", " 351,\n", " 268,\n", " 229,\n", " 210,\n", " 162,\n", " 271,\n", " 245,\n", " 424,\n", " 318,\n", " 246,\n", " 281,\n", " 367,\n", " 461,\n", " 493,\n", " 235,\n", " 240,\n", " 218,\n", " 216,\n", " 217,\n", " 256,\n", " 232,\n", " 253,\n", " 503,\n", " 261,\n", " 311,\n", " 223,\n", " 273,\n", " 262,\n", " 425,\n", " 221,\n", " 249,\n", " 211,\n", " 48,\n", " 241,\n", " 430,\n", " 385,\n", " 326,\n", " 137,\n", " 304,\n", " 198,\n", " 248,\n", " 260,\n", " 233,\n", " 352,\n", " 250,\n", " 267,\n", " 88,\n", " 239,\n", " 214,\n", " 285,\n", " 306,\n", " 149,\n", " 247,\n", " 407,\n", " 295,\n", " 419,\n", " 255,\n", " 213,\n", " 196,\n", " 252,\n", " 364,\n", " 309,\n", " 407,\n", " 300,\n", " 260,\n", " 162,\n", " 271,\n", " 285,\n", " 170,\n", " 304,\n", " 456,\n", " 261,\n", " 306,\n", " 293,\n", " 264,\n", " 45,\n", " 328,\n", " 116,\n", " 243,\n", " 247,\n", " 343,\n", " 203,\n", " 201,\n", " 359,\n", " 305,\n", " 393,\n", " 311,\n", " 213,\n", " 243,\n", " 211,\n", " 232,\n", " 317,\n", " 316,\n", " 245,\n", " 249,\n", " 406,\n", " 127,\n", " 107,\n", " 164,\n", " 162,\n", " 280,\n", " 307,\n", " 525,\n", " 373,\n", " 260,\n", " 306,\n", " 241,\n", " 242,\n", " 162,\n", " 367,\n", " 262,\n", " 211,\n", " 368,\n", " 410,\n", " 464,\n", " 283,\n", " 242,\n", " 218,\n", " 437,\n", " 215,\n", " 375,\n", " 374,\n", " 320,\n", " 497,\n", " 290,\n", " 188,\n", " 339,\n", " 230,\n", " 327,\n", " 120,\n", " 283,\n", " 215,\n", " 384,\n", " 362,\n", " 314,\n", " 276,\n", " 303,\n", " 361,\n", " 195,\n", " 200,\n", " 273,\n", " 309,\n", " 262,\n", " 327,\n", " 293,\n", " 164,\n", " 239,\n", " 279,\n", " 147,\n", " 283,\n", " 317,\n", " 260,\n", " 318,\n", " 250,\n", " 243,\n", " 310,\n", " 214,\n", " 526,\n", " 279,\n", " 350,\n", " 306,\n", " 248,\n", " 462,\n", " 325,\n", " 246,\n", " 247,\n", " 115,\n", " 146,\n", " 73,\n", " 255,\n", " 372,\n", " 240,\n", " 343,\n", " 375,\n", " 206,\n", " 241,\n", " 287,\n", " 277,\n", " 249,\n", " 254,\n", " 438,\n", " 470,\n", " 265,\n", " 211,\n", " 56,\n", " 221,\n", " 94,\n", " 309,\n", " 235,\n", " 153,\n", " 400,\n", " 238,\n", " 349,\n", " 222,\n", " 284,\n", " 262,\n", " 224,\n", " 288,\n", " 79,\n", " 252,\n", " 91,\n", " 271,\n", " 216,\n", " 264,\n", " 133,\n", " 247,\n", " 245,\n", " 316,\n", " 230,\n", " 216,\n", " 245,\n", " 57,\n", " 360,\n", " 270,\n", " 257,\n", " 319,\n", " 252,\n", " 253,\n", " 315,\n", " 475,\n", " 325,\n", " 166,\n", " 221,\n", " 301,\n", " 360,\n", " 257,\n", " 163,\n", " 314,\n", " 322,\n", " 206,\n", " 137,\n", " 405,\n", " 72,\n", " 235,\n", " 132,\n", " 296,\n", " 387,\n", " 199,\n", " 259,\n", " 271,\n", " 270,\n", " 64,\n", " 264,\n", " 357,\n", " 234,\n", " 277,\n", " 423,\n", " 299,\n", " 328,\n", " 301,\n", " 346,\n", " 308,\n", " 260,\n", " 169,\n", " 124,\n", " 400,\n", " 250,\n", " 316,\n", " 101,\n", " 252],\n", " 'ymin': [186,\n", " 91,\n", " 174,\n", " 206,\n", " 122,\n", " 257,\n", " 174,\n", " 126,\n", " 112,\n", " 126,\n", " 173,\n", " 182,\n", " 185,\n", " 175,\n", " 258,\n", " 295,\n", " 275,\n", " 405,\n", " 149,\n", " 134,\n", " 88,\n", " 150,\n", " 148,\n", " 98,\n", " 184,\n", " 143,\n", " 145,\n", " 5,\n", " 146,\n", " 130,\n", " 181,\n", " 196,\n", " 146,\n", " 161,\n", " 117,\n", " 189,\n", " 189,\n", " 169,\n", " 166,\n", " 127,\n", " 165,\n", " 150,\n", " 305,\n", " 115,\n", " 61,\n", " 172,\n", " 131,\n", " 198,\n", " 146,\n", " 116,\n", " 181,\n", " 189,\n", " 171,\n", " 143,\n", " 182,\n", " 228,\n", " 125,\n", " 216,\n", " 136,\n", " 162,\n", " 183,\n", " 186,\n", " 128,\n", " 211,\n", " 369,\n", " 161,\n", " 184,\n", " 228,\n", " 144,\n", " 152,\n", " 182,\n", " 167,\n", " 202,\n", " 134,\n", " 128,\n", " 366,\n", " 147,\n", " 236,\n", " 218,\n", " 210,\n", " 140,\n", " 116,\n", " 191,\n", " 79,\n", " 149,\n", " 138,\n", " 196,\n", " 118,\n", " 128,\n", " 194,\n", " 196,\n", " 195,\n", " 112,\n", " 186,\n", " 118,\n", " 163,\n", " 107,\n", " 146,\n", " 207,\n", " 188,\n", " 224,\n", " 82,\n", " 258,\n", " 118,\n", " 163,\n", " 202,\n", " 191,\n", " 223,\n", " 187,\n", " 208,\n", " 201,\n", " 135,\n", " 140,\n", " 148,\n", " 184,\n", " 66,\n", " 266,\n", " 221,\n", " 217,\n", " 176,\n", " 127,\n", " 145,\n", " 11,\n", " 135,\n", " 184,\n", " 152,\n", " 166,\n", " 102,\n", " 138,\n", " 126,\n", " 166,\n", " 123,\n", " 124,\n", " 185,\n", " 193,\n", " 166,\n", " 249,\n", " 123,\n", " 213,\n", " 139,\n", " 225,\n", " 131,\n", " 148,\n", " 160,\n", " 184,\n", " 138,\n", " 166,\n", " 205,\n", " 162,\n", " 200,\n", " 185,\n", " 147,\n", " 118,\n", " 92,\n", " 165,\n", " 117,\n", " 186,\n", " 202,\n", " 221,\n", " 141,\n", " 162,\n", " 206,\n", " 158,\n", " 131,\n", " 143,\n", " 160,\n", " 126,\n", " 214,\n", " 145,\n", " 264,\n", " 196,\n", " 160,\n", " 180,\n", " 197,\n", " 168,\n", " 79,\n", " 171,\n", " 186,\n", " 84,\n", " 182,\n", " 239,\n", " 141,\n", " 149,\n", " 137,\n", " 122,\n", " 173,\n", " 16,\n", " 175,\n", " 196,\n", " 117,\n", " 166,\n", " 185,\n", " 187,\n", " 112,\n", " 149,\n", " 82,\n", " 157,\n", " 118,\n", " 173,\n", " 156,\n", " 180,\n", " 98,\n", " 195,\n", " 153,\n", " 179,\n", " 135,\n", " 142,\n", " 153,\n", " 98,\n", " 254,\n", " 95,\n", " 115,\n", " 124,\n", " 149,\n", " 187,\n", " 148,\n", " 193,\n", " 121,\n", " 218,\n", " 57,\n", " 154,\n", " 114,\n", " 193,\n", " 159,\n", " 91,\n", " 211,\n", " 287,\n", " 300,\n", " 125,\n", " 138,\n", " 115,\n", " 139,\n", " 122,\n", " 216,\n", " 62,\n", " 231,\n", " 114,\n", " 181,\n", " 150,\n", " 133,\n", " 206,\n", " 263,\n", " 104,\n", " 237,\n", " 126,\n", " 65,\n", " 99,\n", " 132,\n", " 205,\n", " 133,\n", " 159,\n", " 169,\n", " 149,\n", " 82,\n", " 177,\n", " 123,\n", " 176,\n", " 113,\n", " 185,\n", " 113,\n", " 172,\n", " 110,\n", " 150,\n", " 260,\n", " 185,\n", " 193,\n", " 142,\n", " 210,\n", " 283,\n", " 162,\n", " 94,\n", " 110,\n", " 105,\n", " 103,\n", " 169,\n", " 55,\n", " 114,\n", " 104,\n", " 56,\n", " 166,\n", " 218,\n", " 172,\n", " 171,\n", " 128,\n", " 171,\n", " 48,\n", " 167,\n", " 74,\n", " 159,\n", " 146,\n", " 196,\n", " 163,\n", " 205,\n", " 185,\n", " 185,\n", " 256,\n", " 29,\n", " 94,\n", " 168,\n", " 82,\n", " 184,\n", " 254,\n", " 135,\n", " 135,\n", " 114,\n", " 161,\n", " 172,\n", " 171,\n", " 116,\n", " 70,\n", " 173,\n", " 189,\n", " 169,\n", " 206,\n", " 206,\n", " 198,\n", " 100,\n", " 217,\n", " 135,\n", " 144,\n", " 28,\n", " 199,\n", " 22,\n", " 170,\n", " 199,\n", " 141,\n", " 178,\n", " 250,\n", " 75,\n", " 121,\n", " 223,\n", " 148,\n", " 363,\n", " 113,\n", " 129,\n", " 172,\n", " 187,\n", " 114,\n", " 143,\n", " 140,\n", " 225,\n", " 170,\n", " 165,\n", " 229,\n", " 202,\n", " 118,\n", " 193,\n", " 147,\n", " 132,\n", " 116,\n", " 170,\n", " 282,\n", " 147,\n", " 165,\n", " 114,\n", " 195,\n", " 170,\n", " 190,\n", " 163,\n", " 167,\n", " 151,\n", " 220,\n", " 199,\n", " 175,\n", " 223,\n", " 196,\n", " 114,\n", " 163,\n", " 180,\n", " 142,\n", " 182,\n", " 189,\n", " 124,\n", " 201,\n", " 140,\n", " 139,\n", " 132,\n", " 151,\n", " 145,\n", " 104,\n", " 81,\n", " 78,\n", " 169,\n", " 210,\n", " 192,\n", " 173,\n", " 176,\n", " 225,\n", " 111,\n", " 198,\n", " 148,\n", " 90,\n", " 166,\n", " 253,\n", " 110,\n", " 307,\n", " 209,\n", " 87,\n", " 59,\n", " 111,\n", " 237,\n", " 147,\n", " 141,\n", " 187,\n", " 286,\n", " 218,\n", " 158,\n", " 160,\n", " 118,\n", " 64,\n", " 189,\n", " 55,\n", " 194,\n", " 157,\n", " 124,\n", " 197,\n", " 192,\n", " 158,\n", " 216,\n", " 241,\n", " 207,\n", " 180,\n", " 103,\n", " 190,\n", " 67,\n", " 126,\n", " 235,\n", " 150,\n", " 173,\n", " 106,\n", " 170,\n", " 204,\n", " 178],\n", " 'ymax': [211,\n", " 126,\n", " 201,\n", " 221,\n", " 135,\n", " 297,\n", " 235,\n", " 155,\n", " 165,\n", " 182,\n", " 195,\n", " 208,\n", " 217,\n", " 186,\n", " 284,\n", " 341,\n", " 309,\n", " 439,\n", " 167,\n", " 153,\n", " 158,\n", " 209,\n", " 169,\n", " 159,\n", " 211,\n", " 161,\n", " 203,\n", " 148,\n", " 162,\n", " 158,\n", " 191,\n", " 259,\n", " 168,\n", " 192,\n", " 160,\n", " 221,\n", " 197,\n", " 204,\n", " 181,\n", " 159,\n", " 204,\n", " 212,\n", " 370,\n", " 153,\n", " 118,\n", " 186,\n", " 243,\n", " 232,\n", " 159,\n", " 153,\n", " 210,\n", " 209,\n", " 208,\n", " 162,\n", " 206,\n", " 259,\n", " 169,\n", " 228,\n", " 176,\n", " 175,\n", " 202,\n", " 273,\n", " 157,\n", " 246,\n", " 417,\n", " 222,\n", " 213,\n", " 240,\n", " 152,\n", " 179,\n", " 263,\n", " 183,\n", " 222,\n", " 158,\n", " 157,\n", " 384,\n", " 194,\n", " 283,\n", " 235,\n", " 248,\n", " 153,\n", " 132,\n", " 230,\n", " 92,\n", " 209,\n", " 163,\n", " 257,\n", " 136,\n", " 140,\n", " 213,\n", " 231,\n", " 209,\n", " 160,\n", " 211,\n", " 163,\n", " 193,\n", " 135,\n", " 171,\n", " 221,\n", " 205,\n", " 247,\n", " 117,\n", " 284,\n", " 163,\n", " 190,\n", " 220,\n", " 217,\n", " 248,\n", " 207,\n", " 244,\n", " 220,\n", " 159,\n", " 159,\n", " 169,\n", " 205,\n", " 246,\n", " 282,\n", " 242,\n", " 228,\n", " 204,\n", " 143,\n", " 187,\n", " 149,\n", " 150,\n", " 193,\n", " 175,\n", " 188,\n", " 191,\n", " 170,\n", " 137,\n", " 193,\n", " 160,\n", " 144,\n", " 206,\n", " 214,\n", " 185,\n", " 276,\n", " 169,\n", " 267,\n", " 159,\n", " 246,\n", " 154,\n", " 171,\n", " 181,\n", " 211,\n", " 161,\n", " 195,\n", " 226,\n", " 195,\n", " 260,\n", " 195,\n", " 169,\n", " 142,\n", " 137,\n", " 217,\n", " 155,\n", " 208,\n", " 220,\n", " 241,\n", " 185,\n", " 199,\n", " 238,\n", " 217,\n", " 155,\n", " 191,\n", " 180,\n", " 143,\n", " 262,\n", " 196,\n", " 289,\n", " 207,\n", " 194,\n", " 205,\n", " 245,\n", " 213,\n", " 101,\n", " 208,\n", " 209,\n", " 118,\n", " 203,\n", " 286,\n", " 160,\n", " 171,\n", " 174,\n", " 132,\n", " 242,\n", " 256,\n", " 207,\n", " 231,\n", " 135,\n", " 212,\n", " 195,\n", " 225,\n", " 129,\n", " 169,\n", " 253,\n", " 192,\n", " 170,\n", " 203,\n", " 188,\n", " 196,\n", " 149,\n", " 232,\n", " 197,\n", " 188,\n", " 152,\n", " 199,\n", " 271,\n", " 181,\n", " 284,\n", " 125,\n", " 187,\n", " 231,\n", " 170,\n", " 213,\n", " 184,\n", " 248,\n", " 204,\n", " 247,\n", " 76,\n", " 190,\n", " 131,\n", " 229,\n", " 168,\n", " 99,\n", " 237,\n", " 311,\n", " 337,\n", " 173,\n", " 159,\n", " 133,\n", " 222,\n", " 207,\n", " 275,\n", " 161,\n", " 249,\n", " 145,\n", " 206,\n", " 167,\n", " 168,\n", " 240,\n", " 271,\n", " 134,\n", " 367,\n", " 160,\n", " 173,\n", " 201,\n", " 173,\n", " 220,\n", " 159,\n", " 197,\n", " 183,\n", " 170,\n", " 147,\n", " 190,\n", " 150,\n", " 201,\n", " 178,\n", " 271,\n", " 164,\n", " 211,\n", " 159,\n", " 173,\n", " 290,\n", " 215,\n", " 205,\n", " 171,\n", " 239,\n", " 311,\n", " 183,\n", " 107,\n", " 138,\n", " 123,\n", " 175,\n", " 188,\n", " 262,\n", " 239,\n", " 116,\n", " 169,\n", " 187,\n", " 240,\n", " 179,\n", " 208,\n", " 160,\n", " 208,\n", " 129,\n", " 190,\n", " 154,\n", " 183,\n", " 167,\n", " 201,\n", " 220,\n", " 246,\n", " 223,\n", " 226,\n", " 267,\n", " 245,\n", " 141,\n", " 182,\n", " 146,\n", " 232,\n", " 285,\n", " 178,\n", " 169,\n", " 154,\n", " 215,\n", " 202,\n", " 190,\n", " 156,\n", " 155,\n", " 202,\n", " 217,\n", " 182,\n", " 234,\n", " 228,\n", " 271,\n", " 125,\n", " 254,\n", " 191,\n", " 183,\n", " 65,\n", " 217,\n", " 69,\n", " 189,\n", " 271,\n", " 162,\n", " 200,\n", " 275,\n", " 191,\n", " 134,\n", " 244,\n", " 186,\n", " 422,\n", " 180,\n", " 134,\n", " 236,\n", " 220,\n", " 163,\n", " 182,\n", " 167,\n", " 249,\n", " 182,\n", " 192,\n", " 264,\n", " 221,\n", " 138,\n", " 228,\n", " 159,\n", " 166,\n", " 155,\n", " 199,\n", " 310,\n", " 169,\n", " 181,\n", " 233,\n", " 234,\n", " 202,\n", " 198,\n", " 198,\n", " 200,\n", " 178,\n", " 240,\n", " 211,\n", " 212,\n", " 240,\n", " 242,\n", " 156,\n", " 177,\n", " 213,\n", " 176,\n", " 205,\n", " 201,\n", " 135,\n", " 226,\n", " 173,\n", " 159,\n", " 157,\n", " 174,\n", " 167,\n", " 119,\n", " 121,\n", " 102,\n", " 212,\n", " 235,\n", " 203,\n", " 200,\n", " 193,\n", " 266,\n", " 161,\n", " 230,\n", " 169,\n", " 159,\n", " 205,\n", " 280,\n", " 140,\n", " 368,\n", " 239,\n", " 155,\n", " 77,\n", " 134,\n", " 274,\n", " 199,\n", " 154,\n", " 208,\n", " 311,\n", " 225,\n", " 170,\n", " 197,\n", " 210,\n", " 235,\n", " 217,\n", " 78,\n", " 230,\n", " 191,\n", " 137,\n", " 265,\n", " 204,\n", " 196,\n", " 248,\n", " 286,\n", " 232,\n", " 213,\n", " 132,\n", " 224,\n", " 185,\n", " 159,\n", " 246,\n", " 175,\n", " 211,\n", " 119,\n", " 183,\n", " 225,\n", " 203]}" ] }, "metadata": {}, "execution_count": 7 } ] }, { "cell_type": "code", "source": [ "df = pd.DataFrame(labels_dict)\n", "df.to_csv('labels.csv',index=False)\n", "df.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "YvhI7CoMGQmy", "outputId": "72a2d499-0b56-417b-d5f3-73742eed40bf" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " filepath xmin xmax ymin ymax\n", "0 /content/annotations/Cars420.xml 112 160 186 211\n", "1 /content/annotations/Cars380.xml 316 364 91 126\n", "2 /content/annotations/Cars232.xml 175 228 174 201\n", "3 /content/annotations/Cars89.xml 307 345 206 221\n", "4 /content/annotations/Cars221.xml 102 125 122 135" ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
filepathxminxmaxyminymax
0/content/annotations/Cars420.xml112160186211
1/content/annotations/Cars380.xml31636491126
2/content/annotations/Cars232.xml175228174201
3/content/annotations/Cars89.xml307345206221
4/content/annotations/Cars221.xml102125122135
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 9 } ] }, { "cell_type": "code", "source": [ "filename = df['filepath'][0]\n", "def getFilename(filename):\n", " filename_image = xet.parse(filename).getroot().find('filename').text\n", " filepath_image = os.path.join('/content/images',filename_image)\n", " return filepath_image\n", "getFilename(filename)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "id": "0YdW3IRVIITc", "outputId": "026df5a9-2ed2-4c18-a5d4-1787c3c9af1b" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'/content/images/Cars291.png'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 7 } ] }, { "cell_type": "code", "source": [ "# change path\n", "for index, row in df.iterrows():\n", " df.at[index, 'filepath'] = getFilename(df.loc[index, 'filepath'])\n" ], "metadata": { "id": "FWVm6Ee5A58Z" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#Targeting all our values in array selecting all columns\n", "labels = df.iloc[:,1:].values\n", "data = []\n", "output = []\n", "for ind in range(len(df)):\n", " image = df.at[ind, 'filepath']\n", " img_arr = cv2.imread(image)\n", " h,w,d = img_arr.shape\n", " # Prepprocesing\n", " load_image = load_img(image,target_size=(224,224))\n", " load_image_arr = img_to_array(load_image)\n", " norm_load_image_arr = load_image_arr/255.0 # Normalization\n", " # Normalization to labels\n", " xmin,xmax,ymin,ymax = labels[ind]\n", " nxmin,nxmax = xmin/w,xmax/w\n", " nymin,nymax = ymin/h,ymax/h\n", " label_norm = (nxmin,nxmax,nymin,nymax) # Normalized output\n", " # Append\n", " data.append(norm_load_image_arr)\n", " output.append(label_norm)" ], "metadata": { "id": "j0M1gJVBIFr5" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Convert data to array\n", "X = np.array(data,dtype=np.float32)\n", "y = np.array(output,dtype=np.float32)" ], "metadata": { "id": "HnkumNtmIMJK" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "x_use = X[:-33]\n", "y_use = y[:-33]\n", "x_test = X[-33:]\n", "y_test = y[-33:]\n", "x_use.shape, y_use.shape, x_test.shape, y_test.shape," ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bsV21dxiIjl_", "outputId": "ed4061f9-9b4f-416b-b176-dd7354fc9bfa" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "((400, 224, 224, 3), (400, 4), (33, 224, 224, 3), (33, 4))" ] }, "metadata": {}, "execution_count": 18 } ] }, { "cell_type": "code", "source": [ "# Split the data into training and testing set using sklearn.\n", "x_train,x_val,y_train,y_val = train_test_split(x_use,y_use,train_size=0.8,random_state=0)\n", "x_train.shape,x_val.shape,y_train.shape,y_val.shape" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-Ori22rEIwJd", "outputId": "3fc98710-9f35-462a-d52e-7a388fdc66b8" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "((320, 224, 224, 3), (80, 224, 224, 3), (320, 4), (80, 4))" ] }, "metadata": {}, "execution_count": 19 } ] }, { "cell_type": "code", "source": [ "#build TensorDataset\n", "\n", "train_dataset = Data.TensorDataset(torch.from_numpy(x_train).float(), torch.from_numpy(y_train))\n", "val_dataset = Data.TensorDataset(torch.from_numpy(x_val).float(), torch.from_numpy(y_val))" ], "metadata": { "id": "4ri-73FyBrna" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "batch_size = 32\n", "\n", "# train_loader = torch.utils.data.DataLoader(train_dataset,shuffle=True,**kwargs)\n", "# validation_loader = torch.utils.data.DataLoader(validation_dataset,shuffle=True,**kwargs)\n", "\n", "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n", "val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=False)" ], "metadata": { "id": "TA7_5EpVBrta" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# using pretrained model\n", "model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)\n", "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "I0VXeXWCCFyb", "outputId": "a4c61562-4c52-4b10-f0c7-24b2ecda3aea" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=FasterRCNN_ResNet50_FPN_Weights.COCO_V1`. You can also use `weights=FasterRCNN_ResNet50_FPN_Weights.DEFAULT` to get the most up-to-date weights.\n", " warnings.warn(msg)\n", "Downloading: \"https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth\" to /root/.cache/torch/hub/checkpoints/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth\n", "100%|██████████| 160M/160M [00:00<00:00, 266MB/s]\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 2.check sample" ], "metadata": { "id": "s8L4kkRlBsBi" } }, { "cell_type": "code", "source": [ "!unzip \"/content/drive/MyDrive/AA/carimages.zip\"" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "4nsSocftrzkw", "outputId": "7c914b95-5df1-4fb7-dd91-5cfaab077c09" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Archive: /content/drive/MyDrive/AA/carimages.zip\n", " creating: archive (3)/\n", " inflating: __MACOSX/._archive (3) \n", " inflating: archive (3)/car223.jpg \n", " inflating: __MACOSX/archive (3)/._car223.jpg \n", " inflating: archive (3)/car237.jpg \n", " inflating: __MACOSX/archive (3)/._car237.jpg \n", " inflating: archive (3)/car169.jpg \n", " inflating: __MACOSX/archive (3)/._car169.jpg \n", " inflating: archive (3)/car2.jpg \n", " inflating: __MACOSX/archive (3)/._car2.jpg \n", " inflating: archive (3)/car155.jpg \n", " inflating: __MACOSX/archive (3)/._car155.jpg \n", " inflating: archive (3)/car141.jpg \n", " inflating: __MACOSX/archive (3)/._car141.jpg \n", " inflating: archive (3)/car99.jpg \n", " inflating: __MACOSX/archive (3)/._car99.jpg \n", " inflating: archive (3)/car72.jpg \n", " inflating: __MACOSX/archive (3)/._car72.jpg \n", " inflating: archive (3)/car66.jpg \n", " inflating: __MACOSX/archive (3)/._car66.jpg \n", " inflating: archive (3)/car196.jpg \n", " inflating: __MACOSX/archive (3)/._car196.jpg \n", " inflating: archive (3)/car182.jpg \n", " inflating: __MACOSX/archive (3)/._car182.jpg \n", " inflating: archive (3)/car419.jpg \n", " inflating: __MACOSX/archive (3)/._car419.jpg \n", " inflating: archive (3)/car357.jpg \n", " inflating: __MACOSX/archive (3)/._car357.jpg \n", " inflating: archive (3)/car431.jpg \n", " inflating: __MACOSX/archive (3)/._car431.jpg \n", " inflating: archive (3)/car425.jpg \n", " inflating: __MACOSX/archive (3)/._car425.jpg \n", " inflating: archive (3)/car343.jpg \n", " inflating: __MACOSX/archive (3)/._car343.jpg \n", " inflating: archive (3)/car394.jpg \n", " inflating: __MACOSX/archive (3)/._car394.jpg \n", " inflating: archive (3)/car380.jpg \n", " inflating: __MACOSX/archive (3)/._car380.jpg \n", " inflating: archive (3)/car381.jpg \n", " inflating: __MACOSX/archive (3)/._car381.jpg \n", " inflating: archive (3)/car395.jpg \n", " inflating: __MACOSX/archive (3)/._car395.jpg \n", " inflating: archive (3)/car424.jpg \n", " inflating: __MACOSX/archive (3)/._car424.jpg \n", " inflating: archive (3)/car342.jpg \n", " inflating: __MACOSX/archive (3)/._car342.jpg \n", " inflating: archive (3)/car356.jpg \n", " inflating: __MACOSX/archive (3)/._car356.jpg \n", " inflating: archive (3)/car430.jpg \n", " inflating: __MACOSX/archive (3)/._car430.jpg \n", " inflating: archive (3)/car418.jpg \n", " inflating: __MACOSX/archive (3)/._car418.jpg \n", " inflating: archive (3)/car183.jpg \n", " inflating: __MACOSX/archive (3)/._car183.jpg \n", " inflating: archive (3)/car197.jpg \n", " inflating: __MACOSX/archive (3)/._car197.jpg \n", " inflating: archive (3)/car67.jpg \n", " inflating: __MACOSX/archive (3)/._car67.jpg \n", " inflating: archive (3)/car73.jpg \n", " inflating: __MACOSX/archive (3)/._car73.jpg \n", " inflating: archive (3)/car140.jpg \n", " inflating: __MACOSX/archive (3)/._car140.jpg \n", " inflating: archive (3)/car98.jpg \n", " inflating: __MACOSX/archive (3)/._car98.jpg \n", " inflating: archive (3)/car3.jpg \n", " inflating: __MACOSX/archive (3)/._car3.jpg \n", " inflating: archive (3)/car154.jpg \n", " inflating: __MACOSX/archive (3)/._car154.jpg \n", " inflating: archive (3)/car168.jpg \n", " inflating: __MACOSX/archive (3)/._car168.jpg \n", " inflating: archive (3)/car236.jpg \n", " inflating: __MACOSX/archive (3)/._car236.jpg \n", " inflating: archive (3)/car222.jpg \n", " inflating: __MACOSX/archive (3)/._car222.jpg \n", " inflating: archive (3)/car208.jpg \n", " inflating: __MACOSX/archive (3)/._car208.jpg \n", " inflating: archive (3)/car234.jpg \n", " inflating: __MACOSX/archive (3)/._car234.jpg \n", " inflating: archive (3)/car220.jpg \n", " inflating: __MACOSX/archive (3)/._car220.jpg \n", " inflating: archive (3)/car142.jpg \n", " inflating: __MACOSX/archive (3)/._car142.jpg \n", " inflating: archive (3)/car156.jpg \n", " inflating: __MACOSX/archive (3)/._car156.jpg \n", " inflating: archive (3)/car1.jpg \n", " inflating: __MACOSX/archive (3)/._car1.jpg \n", " inflating: archive (3)/car65.jpg \n", " inflating: __MACOSX/archive (3)/._car65.jpg \n", " inflating: archive (3)/car71.jpg \n", " inflating: __MACOSX/archive (3)/._car71.jpg \n", " inflating: archive (3)/car59.jpg \n", " inflating: __MACOSX/archive (3)/._car59.jpg \n", " inflating: archive (3)/car181.jpg \n", " inflating: __MACOSX/archive (3)/._car181.jpg \n", " inflating: archive (3)/car195.jpg \n", " inflating: __MACOSX/archive (3)/._car195.jpg \n", " inflating: archive (3)/car368.jpg \n", " inflating: __MACOSX/archive (3)/._car368.jpg \n", " inflating: archive (3)/car340.jpg \n", " inflating: __MACOSX/archive (3)/._car340.jpg \n", " inflating: archive (3)/car426.jpg \n", " inflating: __MACOSX/archive (3)/._car426.jpg \n", " inflating: archive (3)/car432.jpg \n", " inflating: __MACOSX/archive (3)/._car432.jpg \n", " inflating: archive (3)/car354.jpg \n", " inflating: __MACOSX/archive (3)/._car354.jpg \n", " inflating: archive (3)/car383.jpg \n", " inflating: __MACOSX/archive (3)/._car383.jpg \n", " inflating: archive (3)/car397.jpg \n", " inflating: __MACOSX/archive (3)/._car397.jpg \n", " inflating: archive (3)/car396.jpg \n", " inflating: __MACOSX/archive (3)/._car396.jpg \n", " inflating: archive (3)/car382.jpg \n", " inflating: __MACOSX/archive (3)/._car382.jpg \n", " inflating: archive (3)/car433.jpg \n", " inflating: __MACOSX/archive (3)/._car433.jpg \n", " inflating: archive (3)/car355.jpg \n", " inflating: __MACOSX/archive (3)/._car355.jpg \n", " inflating: archive (3)/car341.jpg \n", " inflating: __MACOSX/archive (3)/._car341.jpg \n", " inflating: archive (3)/car427.jpg \n", " inflating: __MACOSX/archive (3)/._car427.jpg \n", " inflating: archive (3)/car369.jpg \n", " inflating: __MACOSX/archive (3)/._car369.jpg \n", " inflating: archive (3)/car194.jpg \n", " inflating: __MACOSX/archive (3)/._car194.jpg \n", " inflating: archive (3)/car58.jpg \n", " inflating: __MACOSX/archive (3)/._car58.jpg \n", " inflating: archive (3)/car180.jpg \n", " inflating: __MACOSX/archive (3)/._car180.jpg \n", " inflating: archive (3)/car70.jpg \n", " inflating: __MACOSX/archive (3)/._car70.jpg \n", " inflating: archive (3)/car64.jpg \n", " inflating: __MACOSX/archive (3)/._car64.jpg \n", " inflating: archive (3)/car157.jpg \n", " inflating: __MACOSX/archive (3)/._car157.jpg \n", " inflating: archive (3)/car143.jpg \n", " inflating: __MACOSX/archive (3)/._car143.jpg \n", " inflating: archive (3)/car221.jpg \n", " inflating: __MACOSX/archive (3)/._car221.jpg \n", " inflating: archive (3)/car235.jpg \n", " inflating: __MACOSX/archive (3)/._car235.jpg \n", " inflating: archive (3)/car209.jpg \n", " inflating: __MACOSX/archive (3)/._car209.jpg \n", " inflating: archive (3)/car231.jpg \n", " inflating: __MACOSX/archive (3)/._car231.jpg \n", " inflating: archive (3)/car225.jpg \n", " inflating: __MACOSX/archive (3)/._car225.jpg \n", " inflating: archive (3)/car219.jpg \n", " inflating: __MACOSX/archive (3)/._car219.jpg \n", " inflating: archive (3)/car147.jpg \n", " inflating: __MACOSX/archive (3)/._car147.jpg \n", " inflating: archive (3)/car4.jpg \n", " inflating: __MACOSX/archive (3)/._car4.jpg \n", " inflating: archive (3)/car153.jpg \n", " inflating: __MACOSX/archive (3)/._car153.jpg \n", " inflating: archive (3)/car184.jpg \n", " inflating: __MACOSX/archive (3)/._car184.jpg \n", " inflating: archive (3)/car48.jpg \n", " inflating: __MACOSX/archive (3)/._car48.jpg \n", " inflating: archive (3)/car190.jpg \n", " inflating: __MACOSX/archive (3)/._car190.jpg \n", " inflating: archive (3)/car60.jpg \n", " inflating: __MACOSX/archive (3)/._car60.jpg \n", " inflating: archive (3)/car74.jpg \n", " inflating: __MACOSX/archive (3)/._car74.jpg \n", " inflating: archive (3)/car423.jpg \n", " inflating: __MACOSX/archive (3)/._car423.jpg \n", " inflating: archive (3)/.DS_Store \n", " inflating: __MACOSX/archive (3)/._.DS_Store \n", " inflating: archive (3)/car345.jpg \n", " inflating: __MACOSX/archive (3)/._car345.jpg \n", " inflating: archive (3)/car351.jpg \n", " inflating: __MACOSX/archive (3)/._car351.jpg \n", " inflating: archive (3)/car437.jpg \n", " inflating: __MACOSX/archive (3)/._car437.jpg \n", " inflating: archive (3)/car379.jpg \n", " inflating: __MACOSX/archive (3)/._car379.jpg \n", " inflating: archive (3)/car386.jpg \n", " inflating: __MACOSX/archive (3)/._car386.jpg \n", " inflating: archive (3)/car392.jpg \n", " inflating: __MACOSX/archive (3)/._car392.jpg \n", " inflating: archive (3)/car393.jpg \n", " inflating: __MACOSX/archive (3)/._car393.jpg \n", " inflating: archive (3)/car387.jpg \n", " inflating: __MACOSX/archive (3)/._car387.jpg \n", " inflating: archive (3)/car378.jpg \n", " inflating: __MACOSX/archive (3)/._car378.jpg \n", " inflating: archive (3)/car350.jpg \n", " inflating: __MACOSX/archive (3)/._car350.jpg \n", " inflating: archive (3)/car436.jpg \n", " inflating: __MACOSX/archive (3)/._car436.jpg \n", " inflating: archive (3)/car422.jpg \n", " inflating: __MACOSX/archive (3)/._car422.jpg \n", " inflating: archive (3)/car344.jpg \n", " inflating: __MACOSX/archive (3)/._car344.jpg \n", " inflating: archive (3)/car75.jpg \n", " inflating: __MACOSX/archive (3)/._car75.jpg \n", " inflating: archive (3)/car61.jpg \n", " inflating: __MACOSX/archive (3)/._car61.jpg \n", " inflating: archive (3)/car49.jpg \n", " inflating: __MACOSX/archive (3)/._car49.jpg \n", " inflating: archive (3)/car191.jpg \n", " inflating: __MACOSX/archive (3)/._car191.jpg \n", " inflating: archive (3)/car185.jpg \n", " inflating: __MACOSX/archive (3)/._car185.jpg \n", " inflating: archive (3)/car5.jpg \n", " inflating: __MACOSX/archive (3)/._car5.jpg \n", " inflating: archive (3)/car152.jpg \n", " inflating: __MACOSX/archive (3)/._car152.jpg \n", " inflating: archive (3)/car146.jpg \n", " inflating: __MACOSX/archive (3)/._car146.jpg \n", " inflating: archive (3)/car218.jpg \n", " inflating: __MACOSX/archive (3)/._car218.jpg \n", " inflating: archive (3)/car224.jpg \n", " inflating: __MACOSX/archive (3)/._car224.jpg \n", " inflating: archive (3)/car230.jpg \n", " inflating: __MACOSX/archive (3)/._car230.jpg \n", " inflating: archive (3)/car226.jpg \n", " inflating: __MACOSX/archive (3)/._car226.jpg \n", " inflating: archive (3)/car232.jpg \n", " inflating: __MACOSX/archive (3)/._car232.jpg \n", " inflating: archive (3)/car150.jpg \n", " inflating: __MACOSX/archive (3)/._car150.jpg \n", " inflating: archive (3)/car88.jpg \n", " inflating: __MACOSX/archive (3)/._car88.jpg \n", " inflating: archive (3)/car7.jpg \n", " inflating: __MACOSX/archive (3)/._car7.jpg \n", " inflating: archive (3)/car144.jpg \n", " inflating: __MACOSX/archive (3)/._car144.jpg \n", " inflating: archive (3)/car178.jpg \n", " inflating: __MACOSX/archive (3)/._car178.jpg \n", " inflating: archive (3)/car193.jpg \n", " inflating: __MACOSX/archive (3)/._car193.jpg \n", " inflating: archive (3)/car187.jpg \n", " inflating: __MACOSX/archive (3)/._car187.jpg \n", " inflating: archive (3)/car77.jpg \n", " inflating: __MACOSX/archive (3)/._car77.jpg \n", " inflating: archive (3)/car63.jpg \n", " inflating: __MACOSX/archive (3)/._car63.jpg \n", " inflating: archive (3)/car434.jpg \n", " inflating: __MACOSX/archive (3)/._car434.jpg \n", " inflating: archive (3)/car352.jpg \n", " inflating: __MACOSX/archive (3)/._car352.jpg \n", " inflating: archive (3)/car346.jpg \n", " inflating: __MACOSX/archive (3)/._car346.jpg \n", " inflating: archive (3)/car420.jpg \n", " inflating: __MACOSX/archive (3)/._car420.jpg \n", " inflating: archive (3)/car408.jpg \n", " inflating: __MACOSX/archive (3)/._car408.jpg \n", " inflating: archive (3)/car391.jpg \n", " inflating: __MACOSX/archive (3)/._car391.jpg \n", " inflating: archive (3)/car385.jpg \n", " inflating: __MACOSX/archive (3)/._car385.jpg \n", " inflating: archive (3)/car384.jpg \n", " inflating: __MACOSX/archive (3)/._car384.jpg \n", " inflating: archive (3)/car390.jpg \n", " inflating: __MACOSX/archive (3)/._car390.jpg \n", " inflating: archive (3)/car409.jpg \n", " inflating: __MACOSX/archive (3)/._car409.jpg \n", " inflating: archive (3)/car347.jpg \n", " inflating: __MACOSX/archive (3)/._car347.jpg \n", " inflating: archive (3)/car421.jpg \n", " inflating: __MACOSX/archive (3)/._car421.jpg \n", " inflating: archive (3)/car435.jpg \n", " inflating: __MACOSX/archive (3)/._car435.jpg \n", " inflating: archive (3)/car353.jpg \n", " inflating: __MACOSX/archive (3)/._car353.jpg \n", " inflating: archive (3)/car62.jpg \n", " inflating: __MACOSX/archive (3)/._car62.jpg \n", " inflating: archive (3)/car76.jpg \n", " inflating: __MACOSX/archive (3)/._car76.jpg \n", " inflating: archive (3)/car186.jpg \n", " inflating: __MACOSX/archive (3)/._car186.jpg \n", " inflating: archive (3)/car192.jpg \n", " inflating: __MACOSX/archive (3)/._car192.jpg \n", " inflating: archive (3)/car179.jpg \n", " inflating: __MACOSX/archive (3)/._car179.jpg \n", " inflating: archive (3)/car145.jpg \n", " inflating: __MACOSX/archive (3)/._car145.jpg \n", " inflating: archive (3)/car151.jpg \n", " inflating: __MACOSX/archive (3)/._car151.jpg \n", " inflating: archive (3)/car6.jpg \n", " inflating: __MACOSX/archive (3)/._car6.jpg \n", " inflating: archive (3)/car89.jpg \n", " inflating: __MACOSX/archive (3)/._car89.jpg \n", " inflating: archive (3)/car233.jpg \n", " inflating: __MACOSX/archive (3)/._car233.jpg \n", " inflating: archive (3)/car227.jpg \n", " inflating: __MACOSX/archive (3)/._car227.jpg \n", " inflating: archive (3)/car268.jpg \n", " inflating: __MACOSX/archive (3)/._car268.jpg \n", " inflating: archive (3)/car240.jpg \n", " inflating: __MACOSX/archive (3)/._car240.jpg \n", " inflating: archive (3)/car254.jpg \n", " inflating: __MACOSX/archive (3)/._car254.jpg \n", " inflating: archive (3)/car283.jpg \n", " inflating: __MACOSX/archive (3)/._car283.jpg \n", " inflating: archive (3)/car297.jpg \n", " inflating: __MACOSX/archive (3)/._car297.jpg \n", " inflating: archive (3)/car136.jpg \n", " inflating: __MACOSX/archive (3)/._car136.jpg \n", " inflating: archive (3)/car122.jpg \n", " inflating: __MACOSX/archive (3)/._car122.jpg \n", " inflating: archive (3)/car11.jpg \n", " inflating: __MACOSX/archive (3)/._car11.jpg \n", " inflating: archive (3)/car39.jpg \n", " inflating: __MACOSX/archive (3)/._car39.jpg \n", " inflating: archive (3)/car308.jpg \n", " inflating: __MACOSX/archive (3)/._car308.jpg \n", " inflating: archive (3)/car452.jpg \n", " inflating: __MACOSX/archive (3)/._car452.jpg \n", " inflating: archive (3)/car334.jpg \n", " inflating: __MACOSX/archive (3)/._car334.jpg \n", " inflating: archive (3)/car320.jpg \n", " inflating: __MACOSX/archive (3)/._car320.jpg \n", " inflating: archive (3)/car446.jpg \n", " inflating: __MACOSX/archive (3)/._car446.jpg \n", " inflating: archive (3)/car491.jpg \n", " inflating: __MACOSX/archive (3)/._car491.jpg \n", " inflating: archive (3)/car485.jpg \n", " inflating: __MACOSX/archive (3)/._car485.jpg \n", " inflating: archive (3)/car484.jpg \n", " inflating: __MACOSX/archive (3)/._car484.jpg \n", " inflating: archive (3)/car490.jpg \n", " inflating: __MACOSX/archive (3)/._car490.jpg \n", " inflating: archive (3)/car321.jpg \n", " inflating: __MACOSX/archive (3)/._car321.jpg \n", " inflating: archive (3)/car447.jpg \n", " inflating: __MACOSX/archive (3)/._car447.jpg \n", " inflating: archive (3)/car453.jpg \n", " inflating: __MACOSX/archive (3)/._car453.jpg \n", " inflating: archive (3)/car335.jpg \n", " inflating: __MACOSX/archive (3)/._car335.jpg \n", " inflating: archive (3)/car309.jpg \n", " inflating: __MACOSX/archive (3)/._car309.jpg \n", " inflating: archive (3)/car38.jpg \n", " inflating: __MACOSX/archive (3)/._car38.jpg \n", " inflating: archive (3)/car10.jpg \n", " inflating: __MACOSX/archive (3)/._car10.jpg \n", " inflating: archive (3)/car123.jpg \n", " inflating: __MACOSX/archive (3)/._car123.jpg \n", " inflating: archive (3)/car137.jpg \n", " inflating: __MACOSX/archive (3)/._car137.jpg \n", " inflating: archive (3)/car296.jpg \n", " inflating: __MACOSX/archive (3)/._car296.jpg \n", " inflating: archive (3)/car282.jpg \n", " inflating: __MACOSX/archive (3)/._car282.jpg \n", " inflating: archive (3)/car255.jpg \n", " inflating: __MACOSX/archive (3)/._car255.jpg \n", " inflating: archive (3)/car241.jpg \n", " inflating: __MACOSX/archive (3)/._car241.jpg \n", " inflating: archive (3)/car269.jpg \n", " inflating: __MACOSX/archive (3)/._car269.jpg \n", " inflating: archive (3)/car257.jpg \n", " inflating: __MACOSX/archive (3)/._car257.jpg \n", " inflating: archive (3)/car243.jpg \n", " inflating: __MACOSX/archive (3)/._car243.jpg \n", " inflating: archive (3)/car294.jpg \n", " inflating: __MACOSX/archive (3)/._car294.jpg \n", " inflating: archive (3)/car280.jpg \n", " inflating: __MACOSX/archive (3)/._car280.jpg \n", " inflating: archive (3)/car109.jpg \n", " inflating: __MACOSX/archive (3)/._car109.jpg \n", " inflating: archive (3)/car121.jpg \n", " inflating: __MACOSX/archive (3)/._car121.jpg \n", " inflating: archive (3)/car135.jpg \n", " inflating: __MACOSX/archive (3)/._car135.jpg \n", " inflating: archive (3)/car12.jpg \n", " inflating: __MACOSX/archive (3)/._car12.jpg \n", " inflating: archive (3)/car479.jpg \n", " inflating: __MACOSX/archive (3)/._car479.jpg \n", " inflating: archive (3)/car445.jpg \n", " inflating: __MACOSX/archive (3)/._car445.jpg \n", " inflating: archive (3)/car323.jpg \n", " inflating: __MACOSX/archive (3)/._car323.jpg \n", " inflating: archive (3)/car337.jpg \n", " inflating: __MACOSX/archive (3)/._car337.jpg \n", " inflating: archive (3)/car451.jpg \n", " inflating: __MACOSX/archive (3)/._car451.jpg \n", " inflating: archive (3)/car486.jpg \n", " inflating: __MACOSX/archive (3)/._car486.jpg \n", " inflating: archive (3)/car492.jpg \n", " inflating: __MACOSX/archive (3)/._car492.jpg \n", " inflating: archive (3)/car493.jpg \n", " inflating: __MACOSX/archive (3)/._car493.jpg \n", " inflating: archive (3)/car487.jpg \n", " inflating: __MACOSX/archive (3)/._car487.jpg \n", " inflating: archive (3)/car336.jpg \n", " inflating: __MACOSX/archive (3)/._car336.jpg \n", " inflating: archive (3)/car450.jpg \n", " inflating: __MACOSX/archive (3)/._car450.jpg \n", " inflating: archive (3)/car444.jpg \n", " inflating: __MACOSX/archive (3)/._car444.jpg \n", " inflating: archive (3)/Car322.jpg \n", " inflating: __MACOSX/archive (3)/._Car322.jpg \n", " inflating: archive (3)/car478.jpg \n", " inflating: __MACOSX/archive (3)/._car478.jpg \n", " inflating: archive (3)/car13.jpg \n", " inflating: __MACOSX/archive (3)/._car13.jpg \n", " inflating: archive (3)/car134.jpg \n", " inflating: __MACOSX/archive (3)/._car134.jpg \n", " inflating: archive (3)/car120.jpg \n", " inflating: __MACOSX/archive (3)/._car120.jpg \n", " inflating: archive (3)/car108.jpg \n", " inflating: __MACOSX/archive (3)/._car108.jpg \n", " inflating: archive (3)/car281.jpg \n", " inflating: __MACOSX/archive (3)/._car281.jpg \n", " inflating: archive (3)/car295.jpg \n", " inflating: __MACOSX/archive (3)/._car295.jpg \n", " inflating: archive (3)/car242.jpg \n", " inflating: __MACOSX/archive (3)/._car242.jpg \n", " inflating: archive (3)/car256.jpg \n", " inflating: __MACOSX/archive (3)/._car256.jpg \n", " inflating: archive (3)/car252.jpg \n", " inflating: __MACOSX/archive (3)/._car252.jpg \n", " inflating: archive (3)/car246.jpg \n", " inflating: __MACOSX/archive (3)/._car246.jpg \n", " inflating: archive (3)/car291.jpg \n", " inflating: __MACOSX/archive (3)/._car291.jpg \n", " inflating: archive (3)/car285.jpg \n", " inflating: __MACOSX/archive (3)/._car285.jpg \n", " inflating: archive (3)/car124.jpg \n", " inflating: __MACOSX/archive (3)/._car124.jpg \n", " inflating: archive (3)/car130.jpg \n", " inflating: __MACOSX/archive (3)/._car130.jpg \n", " inflating: archive (3)/car118.jpg \n", " inflating: __MACOSX/archive (3)/._car118.jpg \n", " inflating: archive (3)/car17.jpg \n", " inflating: __MACOSX/archive (3)/._car17.jpg \n", " inflating: archive (3)/car326.jpg \n", " inflating: __MACOSX/archive (3)/._car326.jpg \n", " inflating: archive (3)/car440.jpg \n", " inflating: __MACOSX/archive (3)/._car440.jpg \n", " inflating: archive (3)/car454.jpg \n", " inflating: __MACOSX/archive (3)/._car454.jpg \n", " inflating: archive (3)/car332.jpg \n", " inflating: __MACOSX/archive (3)/._car332.jpg \n", " inflating: archive (3)/car468.jpg \n", " inflating: __MACOSX/archive (3)/._car468.jpg \n", " inflating: archive (3)/car483.jpg \n", " inflating: __MACOSX/archive (3)/._car483.jpg \n", " inflating: archive (3)/car497.jpg \n", " inflating: __MACOSX/archive (3)/._car497.jpg \n", " inflating: archive (3)/car496.jpg \n", " inflating: __MACOSX/archive (3)/._car496.jpg \n", " inflating: archive (3)/car482.jpg \n", " inflating: __MACOSX/archive (3)/._car482.jpg \n", " inflating: archive (3)/car455.jpg \n", " inflating: __MACOSX/archive (3)/._car455.jpg \n", " inflating: archive (3)/car333.jpg \n", " inflating: __MACOSX/archive (3)/._car333.jpg \n", " inflating: archive (3)/car327.jpg \n", " inflating: __MACOSX/archive (3)/._car327.jpg \n", " inflating: archive (3)/car441.jpg \n", " inflating: __MACOSX/archive (3)/._car441.jpg \n", " inflating: archive (3)/car16.jpg \n", " inflating: __MACOSX/archive (3)/._car16.jpg \n", " inflating: archive (3)/car119.jpg \n", " inflating: __MACOSX/archive (3)/._car119.jpg \n", " inflating: archive (3)/car131.jpg \n", " inflating: __MACOSX/archive (3)/._car131.jpg \n", " inflating: archive (3)/car125.jpg \n", " inflating: __MACOSX/archive (3)/._car125.jpg \n", " inflating: archive (3)/car284.jpg \n", " inflating: __MACOSX/archive (3)/._car284.jpg \n", " inflating: archive (3)/car290.jpg \n", " inflating: __MACOSX/archive (3)/._car290.jpg \n", " inflating: archive (3)/car247.jpg \n", " inflating: __MACOSX/archive (3)/._car247.jpg \n", " inflating: archive (3)/car253.jpg \n", " inflating: __MACOSX/archive (3)/._car253.jpg \n", " inflating: archive (3)/car245.jpg \n", " inflating: __MACOSX/archive (3)/._car245.jpg \n", " inflating: archive (3)/car251.jpg \n", " inflating: __MACOSX/archive (3)/._car251.jpg \n", " inflating: archive (3)/car279.jpg \n", " inflating: __MACOSX/archive (3)/._car279.jpg \n", " inflating: archive (3)/car286.jpg \n", " inflating: __MACOSX/archive (3)/._car286.jpg \n", " inflating: archive (3)/car292.jpg \n", " inflating: __MACOSX/archive (3)/._car292.jpg \n", " inflating: archive (3)/car133.jpg \n", " inflating: __MACOSX/archive (3)/._car133.jpg \n", " inflating: archive (3)/car127.jpg \n", " inflating: __MACOSX/archive (3)/._car127.jpg \n", " inflating: archive (3)/car28.jpg \n", " inflating: __MACOSX/archive (3)/._car28.jpg \n", " inflating: archive (3)/car14.jpg \n", " inflating: __MACOSX/archive (3)/._car14.jpg \n", " inflating: archive (3)/car331.jpg \n", " inflating: __MACOSX/archive (3)/._car331.jpg \n", " inflating: archive (3)/car457.jpg \n", " inflating: __MACOSX/archive (3)/._car457.jpg \n", " inflating: archive (3)/car443.jpg \n", " inflating: __MACOSX/archive (3)/._car443.jpg \n", " inflating: archive (3)/car325.jpg \n", " inflating: __MACOSX/archive (3)/._car325.jpg \n", " inflating: archive (3)/car319.jpg \n", " inflating: __MACOSX/archive (3)/._car319.jpg \n", " inflating: archive (3)/car494.jpg \n", " inflating: __MACOSX/archive (3)/._car494.jpg \n", " inflating: archive (3)/car480.jpg \n", " inflating: __MACOSX/archive (3)/._car480.jpg \n", " inflating: archive (3)/car481.jpg \n", " inflating: __MACOSX/archive (3)/._car481.jpg \n", " inflating: archive (3)/car495.jpg \n", " inflating: __MACOSX/archive (3)/._car495.jpg \n", " inflating: archive (3)/car318.jpg \n", " inflating: __MACOSX/archive (3)/._car318.jpg \n", " inflating: archive (3)/car442.jpg \n", " inflating: __MACOSX/archive (3)/._car442.jpg \n", " inflating: archive (3)/car324.jpg \n", " inflating: __MACOSX/archive (3)/._car324.jpg \n", " inflating: archive (3)/car330.jpg \n", " inflating: __MACOSX/archive (3)/._car330.jpg \n", " inflating: archive (3)/car456.jpg \n", " inflating: __MACOSX/archive (3)/._car456.jpg \n", " inflating: archive (3)/car15.jpg \n", " inflating: __MACOSX/archive (3)/._car15.jpg \n", " inflating: archive (3)/car29.jpg \n", " inflating: __MACOSX/archive (3)/._car29.jpg \n", " inflating: archive (3)/car126.jpg \n", " inflating: __MACOSX/archive (3)/._car126.jpg \n", " inflating: archive (3)/car132.jpg \n", " inflating: __MACOSX/archive (3)/._car132.jpg \n", " inflating: archive (3)/car293.jpg \n", " inflating: __MACOSX/archive (3)/._car293.jpg \n", " inflating: archive (3)/car287.jpg \n", " inflating: __MACOSX/archive (3)/._car287.jpg \n", " inflating: archive (3)/car278.jpg \n", " inflating: __MACOSX/archive (3)/._car278.jpg \n", " inflating: archive (3)/car250.jpg \n", " inflating: __MACOSX/archive (3)/._car250.jpg \n", " inflating: archive (3)/car244.jpg \n", " inflating: __MACOSX/archive (3)/._car244.jpg \n", " inflating: archive (3)/car249.jpg \n", " inflating: __MACOSX/archive (3)/._car249.jpg \n", " inflating: archive (3)/car261.jpg \n", " inflating: __MACOSX/archive (3)/._car261.jpg \n", " inflating: archive (3)/car275.jpg \n", " inflating: __MACOSX/archive (3)/._car275.jpg \n", " inflating: archive (3)/car117.jpg \n", " inflating: __MACOSX/archive (3)/._car117.jpg \n", " inflating: archive (3)/car103.jpg \n", " inflating: __MACOSX/archive (3)/._car103.jpg \n", " inflating: archive (3)/car30.jpg \n", " inflating: __MACOSX/archive (3)/._car30.jpg \n", " inflating: archive (3)/car24.jpg \n", " inflating: __MACOSX/archive (3)/._car24.jpg \n", " inflating: archive (3)/car18.jpg \n", " inflating: __MACOSX/archive (3)/._car18.jpg \n", " inflating: archive (3)/car329.jpg \n", " inflating: __MACOSX/archive (3)/._car329.jpg \n", " inflating: archive (3)/car473.jpg \n", " inflating: __MACOSX/archive (3)/._car473.jpg \n", " inflating: archive (3)/car315.jpg \n", " inflating: __MACOSX/archive (3)/._car315.jpg \n", " inflating: archive (3)/car301.jpg \n", " inflating: __MACOSX/archive (3)/._car301.jpg \n", " inflating: archive (3)/car467.jpg \n", " inflating: __MACOSX/archive (3)/._car467.jpg \n", " inflating: archive (3)/car498.jpg \n", " inflating: __MACOSX/archive (3)/._car498.jpg \n", " inflating: archive (3)/car499.jpg \n", " inflating: __MACOSX/archive (3)/._car499.jpg \n", " inflating: archive (3)/car300.jpg \n", " inflating: __MACOSX/archive (3)/._car300.jpg \n", " inflating: archive (3)/car466.jpg \n", " inflating: __MACOSX/archive (3)/._car466.jpg \n", " inflating: archive (3)/car472.jpg \n", " inflating: __MACOSX/archive (3)/._car472.jpg \n", " inflating: archive (3)/car314.jpg \n", " inflating: __MACOSX/archive (3)/._car314.jpg \n", " inflating: archive (3)/car328.jpg \n", " inflating: __MACOSX/archive (3)/._car328.jpg \n", " inflating: archive (3)/car19.jpg \n", " inflating: __MACOSX/archive (3)/._car19.jpg \n", " inflating: archive (3)/car25.jpg \n", " inflating: __MACOSX/archive (3)/._car25.jpg \n", " inflating: archive (3)/car31.jpg \n", " inflating: __MACOSX/archive (3)/._car31.jpg \n", " inflating: archive (3)/car102.jpg \n", " inflating: __MACOSX/archive (3)/._car102.jpg \n", " inflating: archive (3)/car116.jpg \n", " inflating: __MACOSX/archive (3)/._car116.jpg \n", " inflating: archive (3)/car274.jpg \n", " inflating: __MACOSX/archive (3)/._car274.jpg \n", " inflating: archive (3)/car260.jpg \n", " inflating: __MACOSX/archive (3)/._car260.jpg \n", " inflating: archive (3)/car248.jpg \n", " inflating: __MACOSX/archive (3)/._car248.jpg \n", " inflating: archive (3)/car276.jpg \n", " inflating: __MACOSX/archive (3)/._car276.jpg \n", " inflating: archive (3)/car262.jpg \n", " inflating: __MACOSX/archive (3)/._car262.jpg \n", " inflating: archive (3)/car289.jpg \n", " inflating: __MACOSX/archive (3)/._car289.jpg \n", " inflating: archive (3)/car128.jpg \n", " inflating: __MACOSX/archive (3)/._car128.jpg \n", " inflating: archive (3)/car100.jpg \n", " inflating: __MACOSX/archive (3)/._car100.jpg \n", " inflating: archive (3)/car114.jpg \n", " inflating: __MACOSX/archive (3)/._car114.jpg \n", " inflating: archive (3)/car27.jpg \n", " inflating: __MACOSX/archive (3)/._car27.jpg \n", " inflating: archive (3)/car33.jpg \n", " inflating: __MACOSX/archive (3)/._car33.jpg \n", " inflating: archive (3)/car458.jpg \n", " inflating: __MACOSX/archive (3)/._car458.jpg \n", " inflating: archive (3)/car464.jpg \n", " inflating: __MACOSX/archive (3)/._car464.jpg \n", " inflating: archive (3)/car302.jpg \n", " inflating: __MACOSX/archive (3)/._car302.jpg \n", " inflating: archive (3)/car316.jpg \n", " inflating: __MACOSX/archive (3)/._car316.jpg \n", " inflating: archive (3)/car470.jpg \n", " inflating: __MACOSX/archive (3)/._car470.jpg \n", " inflating: archive (3)/car317.jpg \n", " inflating: __MACOSX/archive (3)/._car317.jpg \n", " inflating: archive (3)/car471.jpg \n", " inflating: __MACOSX/archive (3)/._car471.jpg \n", " inflating: archive (3)/car465.jpg \n", " inflating: __MACOSX/archive (3)/._car465.jpg \n", " inflating: archive (3)/car303.jpg \n", " inflating: __MACOSX/archive (3)/._car303.jpg \n", " inflating: archive (3)/car459.jpg \n", " inflating: __MACOSX/archive (3)/._car459.jpg \n", " inflating: archive (3)/car32.jpg \n", " inflating: __MACOSX/archive (3)/._car32.jpg \n", " inflating: archive (3)/car26.jpg \n", " inflating: __MACOSX/archive (3)/._car26.jpg \n", " inflating: archive (3)/car115.jpg \n", " inflating: __MACOSX/archive (3)/._car115.jpg \n", " inflating: archive (3)/car101.jpg \n", " inflating: __MACOSX/archive (3)/._car101.jpg \n", " inflating: archive (3)/car129.jpg \n", " inflating: __MACOSX/archive (3)/._car129.jpg \n", " inflating: archive (3)/car288.jpg \n", " inflating: __MACOSX/archive (3)/._car288.jpg \n", " inflating: archive (3)/car263.jpg \n", " inflating: __MACOSX/archive (3)/._car263.jpg \n", " inflating: archive (3)/car277.jpg \n", " inflating: __MACOSX/archive (3)/._car277.jpg \n", " inflating: archive (3)/car273.jpg \n", " inflating: __MACOSX/archive (3)/._car273.jpg \n", " inflating: archive (3)/car267.jpg \n", " inflating: __MACOSX/archive (3)/._car267.jpg \n", " inflating: archive (3)/car298.jpg \n", " inflating: __MACOSX/archive (3)/._car298.jpg \n", " inflating: archive (3)/car105.jpg \n", " inflating: __MACOSX/archive (3)/._car105.jpg \n", " inflating: archive (3)/car111.jpg \n", " inflating: __MACOSX/archive (3)/._car111.jpg \n", " inflating: archive (3)/car139.jpg \n", " inflating: __MACOSX/archive (3)/._car139.jpg \n", " inflating: archive (3)/car22.jpg \n", " inflating: __MACOSX/archive (3)/._car22.jpg \n", " inflating: archive (3)/car36.jpg \n", " inflating: __MACOSX/archive (3)/._car36.jpg \n", " inflating: archive (3)/car307.jpg \n", " inflating: __MACOSX/archive (3)/._car307.jpg \n", " inflating: archive (3)/car461.jpg \n", " inflating: __MACOSX/archive (3)/._car461.jpg \n", " inflating: archive (3)/car475.jpg \n", " inflating: __MACOSX/archive (3)/._car475.jpg \n", " inflating: archive (3)/car313.jpg \n", " inflating: __MACOSX/archive (3)/._car313.jpg \n", " inflating: archive (3)/car449.jpg \n", " inflating: __MACOSX/archive (3)/._car449.jpg \n", " inflating: archive (3)/car448.jpg \n", " inflating: __MACOSX/archive (3)/._car448.jpg \n", " inflating: archive (3)/car474.jpg \n", " inflating: __MACOSX/archive (3)/._car474.jpg \n", " inflating: archive (3)/car312.jpg \n", " inflating: __MACOSX/archive (3)/._car312.jpg \n", " inflating: archive (3)/car306.jpg \n", " inflating: __MACOSX/archive (3)/._car306.jpg \n", " inflating: archive (3)/car460.jpg \n", " inflating: __MACOSX/archive (3)/._car460.jpg \n", " inflating: archive (3)/car37.jpg \n", " inflating: __MACOSX/archive (3)/._car37.jpg \n", " inflating: archive (3)/car23.jpg \n", " inflating: __MACOSX/archive (3)/._car23.jpg \n", " inflating: archive (3)/car138.jpg \n", " inflating: __MACOSX/archive (3)/._car138.jpg \n", " inflating: archive (3)/car110.jpg \n", " inflating: __MACOSX/archive (3)/._car110.jpg \n", " inflating: archive (3)/car104.jpg \n", " inflating: __MACOSX/archive (3)/._car104.jpg \n", " inflating: archive (3)/car299.jpg \n", " inflating: __MACOSX/archive (3)/._car299.jpg \n", " inflating: archive (3)/car500.jpg \n", " inflating: __MACOSX/archive (3)/._car500.jpg \n", " inflating: archive (3)/car266.jpg \n", " inflating: __MACOSX/archive (3)/._car266.jpg \n", " inflating: archive (3)/car272.jpg \n", " inflating: __MACOSX/archive (3)/._car272.jpg \n", " inflating: archive (3)/car264.jpg \n", " inflating: __MACOSX/archive (3)/._car264.jpg \n", " inflating: archive (3)/car270.jpg \n", " inflating: __MACOSX/archive (3)/._car270.jpg \n", " inflating: archive (3)/car258.jpg \n", " inflating: __MACOSX/archive (3)/._car258.jpg \n", " inflating: archive (3)/car112.jpg \n", " inflating: __MACOSX/archive (3)/._car112.jpg \n", " inflating: archive (3)/car106.jpg \n", " inflating: __MACOSX/archive (3)/._car106.jpg \n", " inflating: archive (3)/car35.jpg \n", " inflating: __MACOSX/archive (3)/._car35.jpg \n", " inflating: archive (3)/car21.jpg \n", " inflating: __MACOSX/archive (3)/._car21.jpg \n", " inflating: archive (3)/car310.jpg \n", " inflating: __MACOSX/archive (3)/._car310.jpg \n", " inflating: archive (3)/car476.jpg \n", " inflating: __MACOSX/archive (3)/._car476.jpg \n", " inflating: archive (3)/car462.jpg \n", " inflating: __MACOSX/archive (3)/._car462.jpg \n", " inflating: archive (3)/car304.jpg \n", " inflating: __MACOSX/archive (3)/._car304.jpg \n", " inflating: archive (3)/car338.jpg \n", " inflating: __MACOSX/archive (3)/._car338.jpg \n", " inflating: archive (3)/car489.jpg \n", " inflating: __MACOSX/archive (3)/._car489.jpg \n", " inflating: archive (3)/car488.jpg \n", " inflating: __MACOSX/archive (3)/._car488.jpg \n", " inflating: archive (3)/car339.jpg \n", " inflating: __MACOSX/archive (3)/._car339.jpg \n", " inflating: archive (3)/car463.jpg \n", " inflating: __MACOSX/archive (3)/._car463.jpg \n", " inflating: archive (3)/car305.jpg \n", " inflating: __MACOSX/archive (3)/._car305.jpg \n", " inflating: archive (3)/car311.jpg \n", " inflating: __MACOSX/archive (3)/._car311.jpg \n", " inflating: archive (3)/car477.jpg \n", " inflating: __MACOSX/archive (3)/._car477.jpg \n", " inflating: archive (3)/car20.jpg \n", " inflating: __MACOSX/archive (3)/._car20.jpg \n", " inflating: archive (3)/car34.jpg \n", " inflating: __MACOSX/archive (3)/._car34.jpg \n", " inflating: archive (3)/car107.jpg \n", " inflating: __MACOSX/archive (3)/._car107.jpg \n", " inflating: archive (3)/car113.jpg \n", " inflating: __MACOSX/archive (3)/._car113.jpg \n", " inflating: archive (3)/car259.jpg \n", " inflating: __MACOSX/archive (3)/._car259.jpg \n", " inflating: archive (3)/car271.jpg \n", " inflating: __MACOSX/archive (3)/._car271.jpg \n", " inflating: archive (3)/car265.jpg \n", " inflating: __MACOSX/archive (3)/._car265.jpg \n", " inflating: archive (3)/car202.jpg \n", " inflating: __MACOSX/archive (3)/._car202.jpg \n", " inflating: archive (3)/car216.jpg \n", " inflating: __MACOSX/archive (3)/._car216.jpg \n", " inflating: archive (3)/car22-.jpg \n", " inflating: __MACOSX/archive (3)/._car22-.jpg \n", " inflating: archive (3)/car90.jpg \n", " inflating: __MACOSX/archive (3)/._car90.jpg \n", " inflating: archive (3)/car148.jpg \n", " inflating: __MACOSX/archive (3)/._car148.jpg \n", " inflating: archive (3)/car84.jpg \n", " inflating: __MACOSX/archive (3)/._car84.jpg \n", " inflating: archive (3)/car174.jpg \n", " inflating: __MACOSX/archive (3)/._car174.jpg \n", " inflating: archive (3)/car160.jpg \n", " inflating: __MACOSX/archive (3)/._car160.jpg \n", " inflating: archive (3)/car53.jpg \n", " inflating: __MACOSX/archive (3)/._car53.jpg \n", " inflating: archive (3)/car47.jpg \n", " inflating: __MACOSX/archive (3)/._car47.jpg \n", " inflating: archive (3)/car438.jpg \n", " inflating: __MACOSX/archive (3)/._car438.jpg \n", " inflating: archive (3)/car376.jpg \n", " inflating: __MACOSX/archive (3)/._car376.jpg \n", " inflating: archive (3)/car410.jpg \n", " inflating: __MACOSX/archive (3)/._car410.jpg \n", " inflating: archive (3)/car404.jpg \n", " inflating: __MACOSX/archive (3)/._car404.jpg \n", " inflating: archive (3)/car362.jpg \n", " inflating: __MACOSX/archive (3)/._car362.jpg \n", " inflating: archive (3)/car389.jpg \n", " inflating: __MACOSX/archive (3)/._car389.jpg \n", " inflating: archive (3)/car388.jpg \n", " inflating: __MACOSX/archive (3)/._car388.jpg \n", " inflating: archive (3)/car405.jpg \n", " inflating: __MACOSX/archive (3)/._car405.jpg \n", " inflating: archive (3)/car363.jpg \n", " inflating: __MACOSX/archive (3)/._car363.jpg \n", " inflating: archive (3)/car377.jpg \n", " inflating: __MACOSX/archive (3)/._car377.jpg \n", " inflating: archive (3)/car411.jpg \n", " inflating: __MACOSX/archive (3)/._car411.jpg \n", " inflating: archive (3)/car439.jpg \n", " inflating: __MACOSX/archive (3)/._car439.jpg \n", " inflating: archive (3)/car46.jpg \n", " inflating: __MACOSX/archive (3)/._car46.jpg \n", " inflating: archive (3)/car52.jpg \n", " inflating: __MACOSX/archive (3)/._car52.jpg \n", " inflating: archive (3)/car161.jpg \n", " inflating: __MACOSX/archive (3)/._car161.jpg \n", " inflating: archive (3)/car175.jpg \n", " inflating: __MACOSX/archive (3)/._car175.jpg \n", " inflating: archive (3)/car85.jpg \n", " inflating: __MACOSX/archive (3)/._car85.jpg \n", " inflating: archive (3)/car91.jpg \n", " inflating: __MACOSX/archive (3)/._car91.jpg \n", " inflating: archive (3)/car149.jpg \n", " inflating: __MACOSX/archive (3)/._car149.jpg \n", " inflating: archive (3)/car217.jpg \n", " inflating: __MACOSX/archive (3)/._car217.jpg \n", " inflating: archive (3)/car203.jpg \n", " inflating: __MACOSX/archive (3)/._car203.jpg \n", " inflating: archive (3)/car229.jpg \n", " inflating: __MACOSX/archive (3)/._car229.jpg \n", " inflating: archive (3)/car215.jpg \n", " inflating: __MACOSX/archive (3)/._car215.jpg \n", " inflating: archive (3)/car201.jpg \n", " inflating: __MACOSX/archive (3)/._car201.jpg \n", " inflating: archive (3)/car8.jpg \n", " inflating: __MACOSX/archive (3)/._car8.jpg \n", " inflating: archive (3)/car87.jpg \n", " inflating: __MACOSX/archive (3)/._car87.jpg \n", " inflating: archive (3)/car93.jpg \n", " inflating: __MACOSX/archive (3)/._car93.jpg \n", " inflating: archive (3)/car163.jpg \n", " inflating: __MACOSX/archive (3)/._car163.jpg \n", " inflating: archive (3)/car177.jpg \n", " inflating: __MACOSX/archive (3)/._car177.jpg \n", " inflating: archive (3)/car44.jpg \n", " inflating: __MACOSX/archive (3)/._car44.jpg \n", " inflating: archive (3)/car188.jpg \n", " inflating: __MACOSX/archive (3)/._car188.jpg \n", " inflating: archive (3)/car50.jpg \n", " inflating: __MACOSX/archive (3)/._car50.jpg \n", " inflating: archive (3)/car78.jpg \n", " inflating: __MACOSX/archive (3)/._car78.jpg \n", " inflating: archive (3)/car349.jpg \n", " inflating: __MACOSX/archive (3)/._car349.jpg \n", " inflating: archive (3)/car361.jpg \n", " inflating: __MACOSX/archive (3)/._car361.jpg \n", " inflating: archive (3)/car407.jpg \n", " inflating: __MACOSX/archive (3)/._car407.jpg \n", " inflating: archive (3)/car413.jpg \n", " inflating: __MACOSX/archive (3)/._car413.jpg \n", " inflating: archive (3)/car375.jpg \n", " inflating: __MACOSX/archive (3)/._car375.jpg \n", " inflating: archive (3)/car412.jpg \n", " inflating: __MACOSX/archive (3)/._car412.jpg \n", " inflating: archive (3)/car374.jpg \n", " inflating: __MACOSX/archive (3)/._car374.jpg \n", " inflating: archive (3)/car360.jpg \n", " inflating: __MACOSX/archive (3)/._car360.jpg \n", " inflating: archive (3)/car406.jpg \n", " inflating: __MACOSX/archive (3)/._car406.jpg \n", " inflating: archive (3)/car348.jpg \n", " inflating: __MACOSX/archive (3)/._car348.jpg \n", " inflating: archive (3)/car79.jpg \n", " inflating: __MACOSX/archive (3)/._car79.jpg \n", " inflating: archive (3)/car189.jpg \n", " inflating: __MACOSX/archive (3)/._car189.jpg \n", " inflating: archive (3)/car51.jpg \n", " inflating: __MACOSX/archive (3)/._car51.jpg \n", " inflating: archive (3)/car45.jpg \n", " inflating: __MACOSX/archive (3)/._car45.jpg \n", " inflating: archive (3)/car176.jpg \n", " inflating: __MACOSX/archive (3)/._car176.jpg \n", " inflating: archive (3)/car162.jpg \n", " inflating: __MACOSX/archive (3)/._car162.jpg \n", " inflating: archive (3)/car92.jpg \n", " inflating: __MACOSX/archive (3)/._car92.jpg \n", " inflating: archive (3)/car86.jpg \n", " inflating: __MACOSX/archive (3)/._car86.jpg \n", " inflating: archive (3)/car9.jpg \n", " inflating: __MACOSX/archive (3)/._car9.jpg \n", " inflating: archive (3)/car200.jpg \n", " inflating: __MACOSX/archive (3)/._car200.jpg \n", " inflating: archive (3)/car214.jpg \n", " inflating: __MACOSX/archive (3)/._car214.jpg \n", " inflating: archive (3)/car228.jpg \n", " inflating: __MACOSX/archive (3)/._car228.jpg \n", " inflating: archive (3)/car210.jpg \n", " inflating: __MACOSX/archive (3)/._car210.jpg \n", " inflating: archive (3)/car204.jpg \n", " inflating: __MACOSX/archive (3)/._car204.jpg \n", " inflating: archive (3)/car238.jpg \n", " inflating: __MACOSX/archive (3)/._car238.jpg \n", " inflating: archive (3)/car166.jpg \n", " inflating: __MACOSX/archive (3)/._car166.jpg \n", " inflating: archive (3)/car172.jpg \n", " inflating: __MACOSX/archive (3)/._car172.jpg \n", " inflating: archive (3)/car82.jpg \n", " inflating: __MACOSX/archive (3)/._car82.jpg \n", " inflating: archive (3)/car96.jpg \n", " inflating: __MACOSX/archive (3)/._car96.jpg \n", " inflating: archive (3)/car69.jpg \n", " inflating: __MACOSX/archive (3)/._car69.jpg \n", " inflating: archive (3)/car199.jpg \n", " inflating: __MACOSX/archive (3)/._car199.jpg \n", " inflating: archive (3)/car41.jpg \n", " inflating: __MACOSX/archive (3)/._car41.jpg \n", " inflating: archive (3)/car55.jpg \n", " inflating: __MACOSX/archive (3)/._car55.jpg \n", " inflating: archive (3)/car402.jpg \n", " inflating: __MACOSX/archive (3)/._car402.jpg \n", " inflating: archive (3)/car364.jpg \n", " inflating: __MACOSX/archive (3)/._car364.jpg \n", " inflating: archive (3)/car370.jpg \n", " inflating: __MACOSX/archive (3)/._car370.jpg \n", " inflating: archive (3)/car416.jpg \n", " inflating: __MACOSX/archive (3)/._car416.jpg \n", " inflating: archive (3)/car358.jpg \n", " inflating: __MACOSX/archive (3)/._car358.jpg \n", " inflating: archive (3)/car359.jpg \n", " inflating: __MACOSX/archive (3)/._car359.jpg \n", " inflating: archive (3)/car371.jpg \n", " inflating: __MACOSX/archive (3)/._car371.jpg \n", " inflating: archive (3)/car417.jpg \n", " inflating: __MACOSX/archive (3)/._car417.jpg \n", " inflating: archive (3)/car403.jpg \n", " inflating: __MACOSX/archive (3)/._car403.jpg \n", " inflating: archive (3)/car365.jpg \n", " inflating: __MACOSX/archive (3)/._car365.jpg \n", " inflating: archive (3)/car54.jpg \n", " inflating: __MACOSX/archive (3)/._car54.jpg \n", " inflating: archive (3)/car198.jpg \n", " inflating: __MACOSX/archive (3)/._car198.jpg \n", " inflating: archive (3)/car40.jpg \n", " inflating: __MACOSX/archive (3)/._car40.jpg \n", " inflating: archive (3)/car68.jpg \n", " inflating: __MACOSX/archive (3)/._car68.jpg \n", " inflating: archive (3)/car97.jpg \n", " inflating: __MACOSX/archive (3)/._car97.jpg \n", " inflating: archive (3)/car83.jpg \n", " inflating: __MACOSX/archive (3)/._car83.jpg \n", " inflating: archive (3)/car173.jpg \n", " inflating: __MACOSX/archive (3)/._car173.jpg \n", " inflating: archive (3)/car167.jpg \n", " inflating: __MACOSX/archive (3)/._car167.jpg \n", " inflating: archive (3)/car239.jpg \n", " inflating: __MACOSX/archive (3)/._car239.jpg \n", " inflating: archive (3)/car205.jpg \n", " inflating: __MACOSX/archive (3)/._car205.jpg \n", " inflating: archive (3)/car211.jpg \n", " inflating: __MACOSX/archive (3)/._car211.jpg \n", " inflating: archive (3)/car213.jpg \n", " inflating: __MACOSX/archive (3)/._car213.jpg \n", " inflating: archive (3)/car171.jpg \n", " inflating: __MACOSX/archive (3)/._car171.jpg \n", " inflating: archive (3)/car165.jpg \n", " inflating: __MACOSX/archive (3)/._car165.jpg \n", " inflating: archive (3)/car95.jpg \n", " inflating: __MACOSX/archive (3)/._car95.jpg \n", " inflating: archive (3)/car81.jpg \n", " inflating: __MACOSX/archive (3)/._car81.jpg \n", " inflating: archive (3)/car159.jpg \n", " inflating: __MACOSX/archive (3)/._car159.jpg \n", " inflating: archive (3)/car56.jpg \n", " inflating: __MACOSX/archive (3)/._car56.jpg \n", " inflating: archive (3)/car42.jpg \n", " inflating: __MACOSX/archive (3)/._car42.jpg \n", " inflating: archive (3)/car415.jpg \n", " inflating: __MACOSX/archive (3)/._car415.jpg \n", " inflating: archive (3)/car373.jpg \n", " inflating: __MACOSX/archive (3)/._car373.jpg \n", " inflating: archive (3)/car367.jpg \n", " inflating: __MACOSX/archive (3)/._car367.jpg \n", " inflating: archive (3)/car401.jpg \n", " inflating: __MACOSX/archive (3)/._car401.jpg \n", " inflating: archive (3)/car429.jpg \n", " inflating: __MACOSX/archive (3)/._car429.jpg \n", " inflating: archive (3)/car398.jpg \n", " inflating: __MACOSX/archive (3)/._car398.jpg \n", " inflating: archive (3)/car399.jpg \n", " inflating: __MACOSX/archive (3)/._car399.jpg \n", " inflating: archive (3)/car428.jpg \n", " inflating: __MACOSX/archive (3)/._car428.jpg \n", " inflating: archive (3)/car366.jpg \n", " inflating: __MACOSX/archive (3)/._car366.jpg \n", " inflating: archive (3)/car400.jpg \n", " inflating: __MACOSX/archive (3)/._car400.jpg \n", " inflating: archive (3)/car414.jpg \n", " inflating: __MACOSX/archive (3)/._car414.jpg \n", " inflating: archive (3)/car372.jpg \n", " inflating: __MACOSX/archive (3)/._car372.jpg \n", " inflating: archive (3)/car43.jpg \n", " inflating: __MACOSX/archive (3)/._car43.jpg \n", " inflating: archive (3)/car57.jpg \n", " inflating: __MACOSX/archive (3)/._car57.jpg \n", " inflating: archive (3)/car80.jpg \n", " inflating: __MACOSX/archive (3)/._car80.jpg \n", " inflating: archive (3)/car158.jpg \n", " inflating: __MACOSX/archive (3)/._car158.jpg \n", " inflating: archive (3)/car94.jpg \n", " inflating: __MACOSX/archive (3)/._car94.jpg \n", " inflating: archive (3)/car164.jpg \n", " inflating: __MACOSX/archive (3)/._car164.jpg \n", " inflating: archive (3)/car170.jpg \n", " inflating: __MACOSX/archive (3)/._car170.jpg \n", " inflating: archive (3)/car212.jpg \n", " inflating: __MACOSX/archive (3)/._car212.jpg \n", " inflating: archive (3)/car206.jpg \n", " inflating: __MACOSX/archive (3)/._car206.jpg \n" ] } ] }, { "cell_type": "code", "source": [ "df.loc[344]" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Gbqy3qwjlNW5", "outputId": "820dd7e4-4d37-49d5-887f-c51ff90d475d" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "filepath /content/annotations/Cars106.xml\n", "xmin 138\n", "xmax 177\n", "ymin 79\n", "ymax 92\n", "Name: 344, dtype: object" ] }, "metadata": {}, "execution_count": 83 } ] }, { "cell_type": "code", "source": [ "no = 120\n", "img = io.imread(df.at[no, 'filepath'])\n", "plt.imshow(img)\n", "rect = plt.Rectangle((df['xmin'][no], df['ymin'][no]), df['xmax'][no]-df['xmin'][no], df['ymax'][no]-df['ymin'][no], color='r', fill=False)\n", "plt.gca().add_patch(rect)\n", "\n", "plt.show()\n", "\n", "roi = img[df['ymin'][no]:df['ymax'][no],df['xmin'][no]:df['xmax'][no]]\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 432 }, "id": "CVUxsiyfgO8B", "outputId": "4369c147-424f-4193-d5b7-85f6a5f0ccf0" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Number Plate:\n", "\f\n" ] } ] }, { "cell_type": "code", "source": [ "# extract text from image\n", "text = pt.image_to_string(roi)\n", "print('Number Plate:')\n", "print(text)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JS5ZA9tKoQw8", "outputId": "9508867c-06db-4258-80ce-827230599e96" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "HHIZDE1433\n", "\f\n" ] } ] }, { "cell_type": "code", "source": [ "# Convert data to array\n", "X = np.array(data,dtype=np.float32)\n", "y = np.array(output,dtype=np.float32)" ], "metadata": { "id": "gKad90G6J_Fp" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "X.shape" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ETi4gpClcIpG", "outputId": "55b6547c-0139-4a53-914b-8330f9dc4841" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(433, 224, 224, 3)" ] }, "metadata": {}, "execution_count": 11 } ] }, { "cell_type": "code", "source": [ "y[0][0]" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gkD1oAz6fA2q", "outputId": "3f194f74-f745-496f-ab47-33c21bc05a58" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.13733076" ] }, "metadata": {}, "execution_count": 33 } ] }, { "cell_type": "code", "source": [ "# Split the data into training and testing set using sklearn.\n", "x_train,x_test,y_train,y_test = train_test_split(X,y,train_size=0.8,random_state=0)\n", "x_train.shape,x_test.shape,y_train.shape,y_test.shape" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ChPjpE0CJ_Ic", "outputId": "0845ea02-5702-43d5-994a-fcbd3e4ea712" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "((346, 224, 224, 3), (87, 224, 224, 3), (346, 4), (87, 4))" ] }, "metadata": {}, "execution_count": 15 } ] }, { "cell_type": "markdown", "source": [ "## 3.object detect" ], "metadata": { "id": "wORj0l1xKIbk" } }, { "cell_type": "code", "source": [ "inception_resnet = InceptionResNetV2(weights=\"imagenet\",include_top=False, input_tensor=Input(shape=(224,224,3)))\n", "# ---------------------\n", "headmodel = inception_resnet.output\n", "headmodel = Flatten()(headmodel)\n", "headmodel = Dense(500,activation=\"relu\")(headmodel) # 256\n", "headmodel = Dense(250,activation=\"relu\")(headmodel) # 128\n", "headmodel = Dense(4,activation='sigmoid')(headmodel)# 4\n", "\n", "\n", "# ---------- model\n", "model = Model(inputs=inception_resnet.input,outputs=headmodel)" ], "metadata": { "id": "_VcdgtQvJ_LO" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Complie model\n", "model.compile(loss='mse',optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4))\n", "model.summary()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mPDqhB9xJ_OW", "outputId": "83e42cd5-2a3d-4c7e-8fdc-121f5fe206bb" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Model: \"model\"\n", "__________________________________________________________________________________________________\n", " Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", " input_1 (InputLayer) [(None, 224, 224, 3 0 [] \n", " )] \n", " \n", " conv2d (Conv2D) (None, 111, 111, 32 864 ['input_1[0][0]'] \n", " ) \n", " \n", " batch_normalization (BatchNorm (None, 111, 111, 32 96 ['conv2d[0][0]'] \n", " alization) ) \n", " \n", " activation (Activation) (None, 111, 111, 32 0 ['batch_normalization[0][0]'] \n", " ) \n", " \n", " conv2d_1 (Conv2D) (None, 109, 109, 32 9216 ['activation[0][0]'] \n", " ) \n", " \n", " batch_normalization_1 (BatchNo (None, 109, 109, 32 96 ['conv2d_1[0][0]'] \n", " rmalization) ) \n", " \n", " activation_1 (Activation) (None, 109, 109, 32 0 ['batch_normalization_1[0][0]'] \n", " ) \n", " \n", " conv2d_2 (Conv2D) (None, 109, 109, 64 18432 ['activation_1[0][0]'] \n", " ) \n", " \n", " batch_normalization_2 (BatchNo (None, 109, 109, 64 192 ['conv2d_2[0][0]'] \n", " rmalization) ) \n", " \n", " activation_2 (Activation) (None, 109, 109, 64 0 ['batch_normalization_2[0][0]'] \n", " ) \n", " \n", " max_pooling2d (MaxPooling2D) (None, 54, 54, 64) 0 ['activation_2[0][0]'] \n", " \n", " conv2d_3 (Conv2D) (None, 54, 54, 80) 5120 ['max_pooling2d[0][0]'] \n", " \n", " batch_normalization_3 (BatchNo (None, 54, 54, 80) 240 ['conv2d_3[0][0]'] \n", " rmalization) \n", " \n", " activation_3 (Activation) (None, 54, 54, 80) 0 ['batch_normalization_3[0][0]'] \n", " \n", " conv2d_4 (Conv2D) (None, 52, 52, 192) 138240 ['activation_3[0][0]'] \n", " \n", " batch_normalization_4 (BatchNo (None, 52, 52, 192) 576 ['conv2d_4[0][0]'] \n", " rmalization) \n", " \n", " activation_4 (Activation) (None, 52, 52, 192) 0 ['batch_normalization_4[0][0]'] \n", " \n", " max_pooling2d_1 (MaxPooling2D) (None, 25, 25, 192) 0 ['activation_4[0][0]'] \n", " \n", " conv2d_8 (Conv2D) (None, 25, 25, 64) 12288 ['max_pooling2d_1[0][0]'] \n", " \n", " batch_normalization_8 (BatchNo (None, 25, 25, 64) 192 ['conv2d_8[0][0]'] \n", " rmalization) \n", " \n", " activation_8 (Activation) (None, 25, 25, 64) 0 ['batch_normalization_8[0][0]'] \n", " \n", " conv2d_6 (Conv2D) (None, 25, 25, 48) 9216 ['max_pooling2d_1[0][0]'] \n", " \n", " conv2d_9 (Conv2D) (None, 25, 25, 96) 55296 ['activation_8[0][0]'] \n", " \n", " batch_normalization_6 (BatchNo (None, 25, 25, 48) 144 ['conv2d_6[0][0]'] \n", " rmalization) \n", " \n", " batch_normalization_9 (BatchNo (None, 25, 25, 96) 288 ['conv2d_9[0][0]'] \n", " rmalization) \n", " \n", " activation_6 (Activation) (None, 25, 25, 48) 0 ['batch_normalization_6[0][0]'] \n", " \n", " activation_9 (Activation) (None, 25, 25, 96) 0 ['batch_normalization_9[0][0]'] \n", " \n", " average_pooling2d (AveragePool (None, 25, 25, 192) 0 ['max_pooling2d_1[0][0]'] \n", " ing2D) \n", " \n", " conv2d_5 (Conv2D) (None, 25, 25, 96) 18432 ['max_pooling2d_1[0][0]'] \n", " \n", " conv2d_7 (Conv2D) (None, 25, 25, 64) 76800 ['activation_6[0][0]'] \n", " \n", " conv2d_10 (Conv2D) (None, 25, 25, 96) 82944 ['activation_9[0][0]'] \n", " \n", " conv2d_11 (Conv2D) (None, 25, 25, 64) 12288 ['average_pooling2d[0][0]'] \n", " \n", " batch_normalization_5 (BatchNo (None, 25, 25, 96) 288 ['conv2d_5[0][0]'] \n", " rmalization) \n", " \n", " batch_normalization_7 (BatchNo (None, 25, 25, 64) 192 ['conv2d_7[0][0]'] \n", " rmalization) \n", " \n", " batch_normalization_10 (BatchN (None, 25, 25, 96) 288 ['conv2d_10[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_11 (BatchN (None, 25, 25, 64) 192 ['conv2d_11[0][0]'] \n", " ormalization) \n", " \n", " activation_5 (Activation) (None, 25, 25, 96) 0 ['batch_normalization_5[0][0]'] \n", " \n", " activation_7 (Activation) (None, 25, 25, 64) 0 ['batch_normalization_7[0][0]'] \n", " \n", " activation_10 (Activation) (None, 25, 25, 96) 0 ['batch_normalization_10[0][0]'] \n", " \n", " activation_11 (Activation) (None, 25, 25, 64) 0 ['batch_normalization_11[0][0]'] \n", " \n", " mixed_5b (Concatenate) (None, 25, 25, 320) 0 ['activation_5[0][0]', \n", " 'activation_7[0][0]', \n", " 'activation_10[0][0]', \n", " 'activation_11[0][0]'] \n", " \n", " conv2d_15 (Conv2D) (None, 25, 25, 32) 10240 ['mixed_5b[0][0]'] \n", " \n", " batch_normalization_15 (BatchN (None, 25, 25, 32) 96 ['conv2d_15[0][0]'] \n", " ormalization) \n", " \n", " activation_15 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_15[0][0]'] \n", " \n", " conv2d_13 (Conv2D) (None, 25, 25, 32) 10240 ['mixed_5b[0][0]'] \n", " \n", " conv2d_16 (Conv2D) (None, 25, 25, 48) 13824 ['activation_15[0][0]'] \n", " \n", " batch_normalization_13 (BatchN (None, 25, 25, 32) 96 ['conv2d_13[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_16 (BatchN (None, 25, 25, 48) 144 ['conv2d_16[0][0]'] \n", " ormalization) \n", " \n", " activation_13 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_13[0][0]'] \n", " \n", " activation_16 (Activation) (None, 25, 25, 48) 0 ['batch_normalization_16[0][0]'] \n", " \n", " conv2d_12 (Conv2D) (None, 25, 25, 32) 10240 ['mixed_5b[0][0]'] \n", " \n", " conv2d_14 (Conv2D) (None, 25, 25, 32) 9216 ['activation_13[0][0]'] \n", " \n", " conv2d_17 (Conv2D) (None, 25, 25, 64) 27648 ['activation_16[0][0]'] \n", " \n", " batch_normalization_12 (BatchN (None, 25, 25, 32) 96 ['conv2d_12[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_14 (BatchN (None, 25, 25, 32) 96 ['conv2d_14[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_17 (BatchN (None, 25, 25, 64) 192 ['conv2d_17[0][0]'] \n", " ormalization) \n", " \n", " activation_12 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_12[0][0]'] \n", " \n", " activation_14 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_14[0][0]'] \n", " \n", " activation_17 (Activation) (None, 25, 25, 64) 0 ['batch_normalization_17[0][0]'] \n", " \n", " block35_1_mixed (Concatenate) (None, 25, 25, 128) 0 ['activation_12[0][0]', \n", " 'activation_14[0][0]', \n", " 'activation_17[0][0]'] \n", " \n", " block35_1_conv (Conv2D) (None, 25, 25, 320) 41280 ['block35_1_mixed[0][0]'] \n", " \n", " custom_scale_layer (CustomScal (None, 25, 25, 320) 0 ['mixed_5b[0][0]', \n", " eLayer) 'block35_1_conv[0][0]'] \n", " \n", " block35_1_ac (Activation) (None, 25, 25, 320) 0 ['custom_scale_layer[0][0]'] \n", " \n", " conv2d_21 (Conv2D) (None, 25, 25, 32) 10240 ['block35_1_ac[0][0]'] \n", " \n", " batch_normalization_21 (BatchN (None, 25, 25, 32) 96 ['conv2d_21[0][0]'] \n", " ormalization) \n", " \n", " activation_21 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_21[0][0]'] \n", " \n", " conv2d_19 (Conv2D) (None, 25, 25, 32) 10240 ['block35_1_ac[0][0]'] \n", " \n", " conv2d_22 (Conv2D) (None, 25, 25, 48) 13824 ['activation_21[0][0]'] \n", " \n", " batch_normalization_19 (BatchN (None, 25, 25, 32) 96 ['conv2d_19[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_22 (BatchN (None, 25, 25, 48) 144 ['conv2d_22[0][0]'] \n", " ormalization) \n", " \n", " activation_19 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_19[0][0]'] \n", " \n", " activation_22 (Activation) (None, 25, 25, 48) 0 ['batch_normalization_22[0][0]'] \n", " \n", " conv2d_18 (Conv2D) (None, 25, 25, 32) 10240 ['block35_1_ac[0][0]'] \n", " \n", " conv2d_20 (Conv2D) (None, 25, 25, 32) 9216 ['activation_19[0][0]'] \n", " \n", " conv2d_23 (Conv2D) (None, 25, 25, 64) 27648 ['activation_22[0][0]'] \n", " \n", " batch_normalization_18 (BatchN (None, 25, 25, 32) 96 ['conv2d_18[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_20 (BatchN (None, 25, 25, 32) 96 ['conv2d_20[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_23 (BatchN (None, 25, 25, 64) 192 ['conv2d_23[0][0]'] \n", " ormalization) \n", " \n", " activation_18 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_18[0][0]'] \n", " \n", " activation_20 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_20[0][0]'] \n", " \n", " activation_23 (Activation) (None, 25, 25, 64) 0 ['batch_normalization_23[0][0]'] \n", " \n", " block35_2_mixed (Concatenate) (None, 25, 25, 128) 0 ['activation_18[0][0]', \n", " 'activation_20[0][0]', \n", " 'activation_23[0][0]'] \n", " \n", " block35_2_conv (Conv2D) (None, 25, 25, 320) 41280 ['block35_2_mixed[0][0]'] \n", " \n", " custom_scale_layer_1 (CustomSc (None, 25, 25, 320) 0 ['block35_1_ac[0][0]', \n", " aleLayer) 'block35_2_conv[0][0]'] \n", " \n", " block35_2_ac (Activation) (None, 25, 25, 320) 0 ['custom_scale_layer_1[0][0]'] \n", " \n", " conv2d_27 (Conv2D) (None, 25, 25, 32) 10240 ['block35_2_ac[0][0]'] \n", " \n", " batch_normalization_27 (BatchN (None, 25, 25, 32) 96 ['conv2d_27[0][0]'] \n", " ormalization) \n", " \n", " activation_27 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_27[0][0]'] \n", " \n", " conv2d_25 (Conv2D) (None, 25, 25, 32) 10240 ['block35_2_ac[0][0]'] \n", " \n", " conv2d_28 (Conv2D) (None, 25, 25, 48) 13824 ['activation_27[0][0]'] \n", " \n", " batch_normalization_25 (BatchN (None, 25, 25, 32) 96 ['conv2d_25[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_28 (BatchN (None, 25, 25, 48) 144 ['conv2d_28[0][0]'] \n", " ormalization) \n", " \n", " activation_25 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_25[0][0]'] \n", " \n", " activation_28 (Activation) (None, 25, 25, 48) 0 ['batch_normalization_28[0][0]'] \n", " \n", " conv2d_24 (Conv2D) (None, 25, 25, 32) 10240 ['block35_2_ac[0][0]'] \n", " \n", " conv2d_26 (Conv2D) (None, 25, 25, 32) 9216 ['activation_25[0][0]'] \n", " \n", " conv2d_29 (Conv2D) (None, 25, 25, 64) 27648 ['activation_28[0][0]'] \n", " \n", " batch_normalization_24 (BatchN (None, 25, 25, 32) 96 ['conv2d_24[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_26 (BatchN (None, 25, 25, 32) 96 ['conv2d_26[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_29 (BatchN (None, 25, 25, 64) 192 ['conv2d_29[0][0]'] \n", " ormalization) \n", " \n", " activation_24 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_24[0][0]'] \n", " \n", " activation_26 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_26[0][0]'] \n", " \n", " activation_29 (Activation) (None, 25, 25, 64) 0 ['batch_normalization_29[0][0]'] \n", " \n", " block35_3_mixed (Concatenate) (None, 25, 25, 128) 0 ['activation_24[0][0]', \n", " 'activation_26[0][0]', \n", " 'activation_29[0][0]'] \n", " \n", " block35_3_conv (Conv2D) (None, 25, 25, 320) 41280 ['block35_3_mixed[0][0]'] \n", " \n", " custom_scale_layer_2 (CustomSc (None, 25, 25, 320) 0 ['block35_2_ac[0][0]', \n", " aleLayer) 'block35_3_conv[0][0]'] \n", " \n", " block35_3_ac (Activation) (None, 25, 25, 320) 0 ['custom_scale_layer_2[0][0]'] \n", " \n", " conv2d_33 (Conv2D) (None, 25, 25, 32) 10240 ['block35_3_ac[0][0]'] \n", " \n", " batch_normalization_33 (BatchN (None, 25, 25, 32) 96 ['conv2d_33[0][0]'] \n", " ormalization) \n", " \n", " activation_33 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_33[0][0]'] \n", " \n", " conv2d_31 (Conv2D) (None, 25, 25, 32) 10240 ['block35_3_ac[0][0]'] \n", " \n", " conv2d_34 (Conv2D) (None, 25, 25, 48) 13824 ['activation_33[0][0]'] \n", " \n", " batch_normalization_31 (BatchN (None, 25, 25, 32) 96 ['conv2d_31[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_34 (BatchN (None, 25, 25, 48) 144 ['conv2d_34[0][0]'] \n", " ormalization) \n", " \n", " activation_31 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_31[0][0]'] \n", " \n", " activation_34 (Activation) (None, 25, 25, 48) 0 ['batch_normalization_34[0][0]'] \n", " \n", " conv2d_30 (Conv2D) (None, 25, 25, 32) 10240 ['block35_3_ac[0][0]'] \n", " \n", " conv2d_32 (Conv2D) (None, 25, 25, 32) 9216 ['activation_31[0][0]'] \n", " \n", " conv2d_35 (Conv2D) (None, 25, 25, 64) 27648 ['activation_34[0][0]'] \n", " \n", " batch_normalization_30 (BatchN (None, 25, 25, 32) 96 ['conv2d_30[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_32 (BatchN (None, 25, 25, 32) 96 ['conv2d_32[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_35 (BatchN (None, 25, 25, 64) 192 ['conv2d_35[0][0]'] \n", " ormalization) \n", " \n", " activation_30 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_30[0][0]'] \n", " \n", " activation_32 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_32[0][0]'] \n", " \n", " activation_35 (Activation) (None, 25, 25, 64) 0 ['batch_normalization_35[0][0]'] \n", " \n", " block35_4_mixed (Concatenate) (None, 25, 25, 128) 0 ['activation_30[0][0]', \n", " 'activation_32[0][0]', \n", " 'activation_35[0][0]'] \n", " \n", " block35_4_conv (Conv2D) (None, 25, 25, 320) 41280 ['block35_4_mixed[0][0]'] \n", " \n", " custom_scale_layer_3 (CustomSc (None, 25, 25, 320) 0 ['block35_3_ac[0][0]', \n", " aleLayer) 'block35_4_conv[0][0]'] \n", " \n", " block35_4_ac (Activation) (None, 25, 25, 320) 0 ['custom_scale_layer_3[0][0]'] \n", " \n", " conv2d_39 (Conv2D) (None, 25, 25, 32) 10240 ['block35_4_ac[0][0]'] \n", " \n", " batch_normalization_39 (BatchN (None, 25, 25, 32) 96 ['conv2d_39[0][0]'] \n", " ormalization) \n", " \n", " activation_39 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_39[0][0]'] \n", " \n", " conv2d_37 (Conv2D) (None, 25, 25, 32) 10240 ['block35_4_ac[0][0]'] \n", " \n", " conv2d_40 (Conv2D) (None, 25, 25, 48) 13824 ['activation_39[0][0]'] \n", " \n", " batch_normalization_37 (BatchN (None, 25, 25, 32) 96 ['conv2d_37[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_40 (BatchN (None, 25, 25, 48) 144 ['conv2d_40[0][0]'] \n", " ormalization) \n", " \n", " activation_37 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_37[0][0]'] \n", " \n", " activation_40 (Activation) (None, 25, 25, 48) 0 ['batch_normalization_40[0][0]'] \n", " \n", " conv2d_36 (Conv2D) (None, 25, 25, 32) 10240 ['block35_4_ac[0][0]'] \n", " \n", " conv2d_38 (Conv2D) (None, 25, 25, 32) 9216 ['activation_37[0][0]'] \n", " \n", " conv2d_41 (Conv2D) (None, 25, 25, 64) 27648 ['activation_40[0][0]'] \n", " \n", " batch_normalization_36 (BatchN (None, 25, 25, 32) 96 ['conv2d_36[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_38 (BatchN (None, 25, 25, 32) 96 ['conv2d_38[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_41 (BatchN (None, 25, 25, 64) 192 ['conv2d_41[0][0]'] \n", " ormalization) \n", " \n", " activation_36 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_36[0][0]'] \n", " \n", " activation_38 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_38[0][0]'] \n", " \n", " activation_41 (Activation) (None, 25, 25, 64) 0 ['batch_normalization_41[0][0]'] \n", " \n", " block35_5_mixed (Concatenate) (None, 25, 25, 128) 0 ['activation_36[0][0]', \n", " 'activation_38[0][0]', \n", " 'activation_41[0][0]'] \n", " \n", " block35_5_conv (Conv2D) (None, 25, 25, 320) 41280 ['block35_5_mixed[0][0]'] \n", " \n", " custom_scale_layer_4 (CustomSc (None, 25, 25, 320) 0 ['block35_4_ac[0][0]', \n", " aleLayer) 'block35_5_conv[0][0]'] \n", " \n", " block35_5_ac (Activation) (None, 25, 25, 320) 0 ['custom_scale_layer_4[0][0]'] \n", " \n", " conv2d_45 (Conv2D) (None, 25, 25, 32) 10240 ['block35_5_ac[0][0]'] \n", " \n", " batch_normalization_45 (BatchN (None, 25, 25, 32) 96 ['conv2d_45[0][0]'] \n", " ormalization) \n", " \n", " activation_45 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_45[0][0]'] \n", " \n", " conv2d_43 (Conv2D) (None, 25, 25, 32) 10240 ['block35_5_ac[0][0]'] \n", " \n", " conv2d_46 (Conv2D) (None, 25, 25, 48) 13824 ['activation_45[0][0]'] \n", " \n", " batch_normalization_43 (BatchN (None, 25, 25, 32) 96 ['conv2d_43[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_46 (BatchN (None, 25, 25, 48) 144 ['conv2d_46[0][0]'] \n", " ormalization) \n", " \n", " activation_43 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_43[0][0]'] \n", " \n", " activation_46 (Activation) (None, 25, 25, 48) 0 ['batch_normalization_46[0][0]'] \n", " \n", " conv2d_42 (Conv2D) (None, 25, 25, 32) 10240 ['block35_5_ac[0][0]'] \n", " \n", " conv2d_44 (Conv2D) (None, 25, 25, 32) 9216 ['activation_43[0][0]'] \n", " \n", " conv2d_47 (Conv2D) (None, 25, 25, 64) 27648 ['activation_46[0][0]'] \n", " \n", " batch_normalization_42 (BatchN (None, 25, 25, 32) 96 ['conv2d_42[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_44 (BatchN (None, 25, 25, 32) 96 ['conv2d_44[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_47 (BatchN (None, 25, 25, 64) 192 ['conv2d_47[0][0]'] \n", " ormalization) \n", " \n", " activation_42 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_42[0][0]'] \n", " \n", " activation_44 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_44[0][0]'] \n", " \n", " activation_47 (Activation) (None, 25, 25, 64) 0 ['batch_normalization_47[0][0]'] \n", " \n", " block35_6_mixed (Concatenate) (None, 25, 25, 128) 0 ['activation_42[0][0]', \n", " 'activation_44[0][0]', \n", " 'activation_47[0][0]'] \n", " \n", " block35_6_conv (Conv2D) (None, 25, 25, 320) 41280 ['block35_6_mixed[0][0]'] \n", " \n", " custom_scale_layer_5 (CustomSc (None, 25, 25, 320) 0 ['block35_5_ac[0][0]', \n", " aleLayer) 'block35_6_conv[0][0]'] \n", " \n", " block35_6_ac (Activation) (None, 25, 25, 320) 0 ['custom_scale_layer_5[0][0]'] \n", " \n", " conv2d_51 (Conv2D) (None, 25, 25, 32) 10240 ['block35_6_ac[0][0]'] \n", " \n", " batch_normalization_51 (BatchN (None, 25, 25, 32) 96 ['conv2d_51[0][0]'] \n", " ormalization) \n", " \n", " activation_51 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_51[0][0]'] \n", " \n", " conv2d_49 (Conv2D) (None, 25, 25, 32) 10240 ['block35_6_ac[0][0]'] \n", " \n", " conv2d_52 (Conv2D) (None, 25, 25, 48) 13824 ['activation_51[0][0]'] \n", " \n", " batch_normalization_49 (BatchN (None, 25, 25, 32) 96 ['conv2d_49[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_52 (BatchN (None, 25, 25, 48) 144 ['conv2d_52[0][0]'] \n", " ormalization) \n", " \n", " activation_49 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_49[0][0]'] \n", " \n", " activation_52 (Activation) (None, 25, 25, 48) 0 ['batch_normalization_52[0][0]'] \n", " \n", " conv2d_48 (Conv2D) (None, 25, 25, 32) 10240 ['block35_6_ac[0][0]'] \n", " \n", " conv2d_50 (Conv2D) (None, 25, 25, 32) 9216 ['activation_49[0][0]'] \n", " \n", " conv2d_53 (Conv2D) (None, 25, 25, 64) 27648 ['activation_52[0][0]'] \n", " \n", " batch_normalization_48 (BatchN (None, 25, 25, 32) 96 ['conv2d_48[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_50 (BatchN (None, 25, 25, 32) 96 ['conv2d_50[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_53 (BatchN (None, 25, 25, 64) 192 ['conv2d_53[0][0]'] \n", " ormalization) \n", " \n", " activation_48 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_48[0][0]'] \n", " \n", " activation_50 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_50[0][0]'] \n", " \n", " activation_53 (Activation) (None, 25, 25, 64) 0 ['batch_normalization_53[0][0]'] \n", " \n", " block35_7_mixed (Concatenate) (None, 25, 25, 128) 0 ['activation_48[0][0]', \n", " 'activation_50[0][0]', \n", " 'activation_53[0][0]'] \n", " \n", " block35_7_conv (Conv2D) (None, 25, 25, 320) 41280 ['block35_7_mixed[0][0]'] \n", " \n", " custom_scale_layer_6 (CustomSc (None, 25, 25, 320) 0 ['block35_6_ac[0][0]', \n", " aleLayer) 'block35_7_conv[0][0]'] \n", " \n", " block35_7_ac (Activation) (None, 25, 25, 320) 0 ['custom_scale_layer_6[0][0]'] \n", " \n", " conv2d_57 (Conv2D) (None, 25, 25, 32) 10240 ['block35_7_ac[0][0]'] \n", " \n", " batch_normalization_57 (BatchN (None, 25, 25, 32) 96 ['conv2d_57[0][0]'] \n", " ormalization) \n", " \n", " activation_57 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_57[0][0]'] \n", " \n", " conv2d_55 (Conv2D) (None, 25, 25, 32) 10240 ['block35_7_ac[0][0]'] \n", " \n", " conv2d_58 (Conv2D) (None, 25, 25, 48) 13824 ['activation_57[0][0]'] \n", " \n", " batch_normalization_55 (BatchN (None, 25, 25, 32) 96 ['conv2d_55[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_58 (BatchN (None, 25, 25, 48) 144 ['conv2d_58[0][0]'] \n", " ormalization) \n", " \n", " activation_55 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_55[0][0]'] \n", " \n", " activation_58 (Activation) (None, 25, 25, 48) 0 ['batch_normalization_58[0][0]'] \n", " \n", " conv2d_54 (Conv2D) (None, 25, 25, 32) 10240 ['block35_7_ac[0][0]'] \n", " \n", " conv2d_56 (Conv2D) (None, 25, 25, 32) 9216 ['activation_55[0][0]'] \n", " \n", " conv2d_59 (Conv2D) (None, 25, 25, 64) 27648 ['activation_58[0][0]'] \n", " \n", " batch_normalization_54 (BatchN (None, 25, 25, 32) 96 ['conv2d_54[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_56 (BatchN (None, 25, 25, 32) 96 ['conv2d_56[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_59 (BatchN (None, 25, 25, 64) 192 ['conv2d_59[0][0]'] \n", " ormalization) \n", " \n", " activation_54 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_54[0][0]'] \n", " \n", " activation_56 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_56[0][0]'] \n", " \n", " activation_59 (Activation) (None, 25, 25, 64) 0 ['batch_normalization_59[0][0]'] \n", " \n", " block35_8_mixed (Concatenate) (None, 25, 25, 128) 0 ['activation_54[0][0]', \n", " 'activation_56[0][0]', \n", " 'activation_59[0][0]'] \n", " \n", " block35_8_conv (Conv2D) (None, 25, 25, 320) 41280 ['block35_8_mixed[0][0]'] \n", " \n", " custom_scale_layer_7 (CustomSc (None, 25, 25, 320) 0 ['block35_7_ac[0][0]', \n", " aleLayer) 'block35_8_conv[0][0]'] \n", " \n", " block35_8_ac (Activation) (None, 25, 25, 320) 0 ['custom_scale_layer_7[0][0]'] \n", " \n", " conv2d_63 (Conv2D) (None, 25, 25, 32) 10240 ['block35_8_ac[0][0]'] \n", " \n", " batch_normalization_63 (BatchN (None, 25, 25, 32) 96 ['conv2d_63[0][0]'] \n", " ormalization) \n", " \n", " activation_63 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_63[0][0]'] \n", " \n", " conv2d_61 (Conv2D) (None, 25, 25, 32) 10240 ['block35_8_ac[0][0]'] \n", " \n", " conv2d_64 (Conv2D) (None, 25, 25, 48) 13824 ['activation_63[0][0]'] \n", " \n", " batch_normalization_61 (BatchN (None, 25, 25, 32) 96 ['conv2d_61[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_64 (BatchN (None, 25, 25, 48) 144 ['conv2d_64[0][0]'] \n", " ormalization) \n", " \n", " activation_61 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_61[0][0]'] \n", " \n", " activation_64 (Activation) (None, 25, 25, 48) 0 ['batch_normalization_64[0][0]'] \n", " \n", " conv2d_60 (Conv2D) (None, 25, 25, 32) 10240 ['block35_8_ac[0][0]'] \n", " \n", " conv2d_62 (Conv2D) (None, 25, 25, 32) 9216 ['activation_61[0][0]'] \n", " \n", " conv2d_65 (Conv2D) (None, 25, 25, 64) 27648 ['activation_64[0][0]'] \n", " \n", " batch_normalization_60 (BatchN (None, 25, 25, 32) 96 ['conv2d_60[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_62 (BatchN (None, 25, 25, 32) 96 ['conv2d_62[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_65 (BatchN (None, 25, 25, 64) 192 ['conv2d_65[0][0]'] \n", " ormalization) \n", " \n", " activation_60 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_60[0][0]'] \n", " \n", " activation_62 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_62[0][0]'] \n", " \n", " activation_65 (Activation) (None, 25, 25, 64) 0 ['batch_normalization_65[0][0]'] \n", " \n", " block35_9_mixed (Concatenate) (None, 25, 25, 128) 0 ['activation_60[0][0]', \n", " 'activation_62[0][0]', \n", " 'activation_65[0][0]'] \n", " \n", " block35_9_conv (Conv2D) (None, 25, 25, 320) 41280 ['block35_9_mixed[0][0]'] \n", " \n", " custom_scale_layer_8 (CustomSc (None, 25, 25, 320) 0 ['block35_8_ac[0][0]', \n", " aleLayer) 'block35_9_conv[0][0]'] \n", " \n", " block35_9_ac (Activation) (None, 25, 25, 320) 0 ['custom_scale_layer_8[0][0]'] \n", " \n", " conv2d_69 (Conv2D) (None, 25, 25, 32) 10240 ['block35_9_ac[0][0]'] \n", " \n", " batch_normalization_69 (BatchN (None, 25, 25, 32) 96 ['conv2d_69[0][0]'] \n", " ormalization) \n", " \n", " activation_69 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_69[0][0]'] \n", " \n", " conv2d_67 (Conv2D) (None, 25, 25, 32) 10240 ['block35_9_ac[0][0]'] \n", " \n", " conv2d_70 (Conv2D) (None, 25, 25, 48) 13824 ['activation_69[0][0]'] \n", " \n", " batch_normalization_67 (BatchN (None, 25, 25, 32) 96 ['conv2d_67[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_70 (BatchN (None, 25, 25, 48) 144 ['conv2d_70[0][0]'] \n", " ormalization) \n", " \n", " activation_67 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_67[0][0]'] \n", " \n", " activation_70 (Activation) (None, 25, 25, 48) 0 ['batch_normalization_70[0][0]'] \n", " \n", " conv2d_66 (Conv2D) (None, 25, 25, 32) 10240 ['block35_9_ac[0][0]'] \n", " \n", " conv2d_68 (Conv2D) (None, 25, 25, 32) 9216 ['activation_67[0][0]'] \n", " \n", " conv2d_71 (Conv2D) (None, 25, 25, 64) 27648 ['activation_70[0][0]'] \n", " \n", " batch_normalization_66 (BatchN (None, 25, 25, 32) 96 ['conv2d_66[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_68 (BatchN (None, 25, 25, 32) 96 ['conv2d_68[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_71 (BatchN (None, 25, 25, 64) 192 ['conv2d_71[0][0]'] \n", " ormalization) \n", " \n", " activation_66 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_66[0][0]'] \n", " \n", " activation_68 (Activation) (None, 25, 25, 32) 0 ['batch_normalization_68[0][0]'] \n", " \n", " activation_71 (Activation) (None, 25, 25, 64) 0 ['batch_normalization_71[0][0]'] \n", " \n", " block35_10_mixed (Concatenate) (None, 25, 25, 128) 0 ['activation_66[0][0]', \n", " 'activation_68[0][0]', \n", " 'activation_71[0][0]'] \n", " \n", " block35_10_conv (Conv2D) (None, 25, 25, 320) 41280 ['block35_10_mixed[0][0]'] \n", " \n", " custom_scale_layer_9 (CustomSc (None, 25, 25, 320) 0 ['block35_9_ac[0][0]', \n", " aleLayer) 'block35_10_conv[0][0]'] \n", " \n", " block35_10_ac (Activation) (None, 25, 25, 320) 0 ['custom_scale_layer_9[0][0]'] \n", " \n", " conv2d_73 (Conv2D) (None, 25, 25, 256) 81920 ['block35_10_ac[0][0]'] \n", " \n", " batch_normalization_73 (BatchN (None, 25, 25, 256) 768 ['conv2d_73[0][0]'] \n", " ormalization) \n", " \n", " activation_73 (Activation) (None, 25, 25, 256) 0 ['batch_normalization_73[0][0]'] \n", " \n", " conv2d_74 (Conv2D) (None, 25, 25, 256) 589824 ['activation_73[0][0]'] \n", " \n", " batch_normalization_74 (BatchN (None, 25, 25, 256) 768 ['conv2d_74[0][0]'] \n", " ormalization) \n", " \n", " activation_74 (Activation) (None, 25, 25, 256) 0 ['batch_normalization_74[0][0]'] \n", " \n", " conv2d_72 (Conv2D) (None, 12, 12, 384) 1105920 ['block35_10_ac[0][0]'] \n", " \n", " conv2d_75 (Conv2D) (None, 12, 12, 384) 884736 ['activation_74[0][0]'] \n", " \n", " batch_normalization_72 (BatchN (None, 12, 12, 384) 1152 ['conv2d_72[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_75 (BatchN (None, 12, 12, 384) 1152 ['conv2d_75[0][0]'] \n", " ormalization) \n", " \n", " activation_72 (Activation) (None, 12, 12, 384) 0 ['batch_normalization_72[0][0]'] \n", " \n", " activation_75 (Activation) (None, 12, 12, 384) 0 ['batch_normalization_75[0][0]'] \n", " \n", " max_pooling2d_2 (MaxPooling2D) (None, 12, 12, 320) 0 ['block35_10_ac[0][0]'] \n", " \n", " mixed_6a (Concatenate) (None, 12, 12, 1088 0 ['activation_72[0][0]', \n", " ) 'activation_75[0][0]', \n", " 'max_pooling2d_2[0][0]'] \n", " \n", " conv2d_77 (Conv2D) (None, 12, 12, 128) 139264 ['mixed_6a[0][0]'] \n", " \n", " batch_normalization_77 (BatchN (None, 12, 12, 128) 384 ['conv2d_77[0][0]'] \n", " ormalization) \n", " \n", " activation_77 (Activation) (None, 12, 12, 128) 0 ['batch_normalization_77[0][0]'] \n", " \n", " conv2d_78 (Conv2D) (None, 12, 12, 160) 143360 ['activation_77[0][0]'] \n", " \n", " batch_normalization_78 (BatchN (None, 12, 12, 160) 480 ['conv2d_78[0][0]'] \n", " ormalization) \n", " \n", " activation_78 (Activation) (None, 12, 12, 160) 0 ['batch_normalization_78[0][0]'] \n", " \n", " conv2d_76 (Conv2D) (None, 12, 12, 192) 208896 ['mixed_6a[0][0]'] \n", " \n", " conv2d_79 (Conv2D) (None, 12, 12, 192) 215040 ['activation_78[0][0]'] \n", " \n", " batch_normalization_76 (BatchN (None, 12, 12, 192) 576 ['conv2d_76[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_79 (BatchN (None, 12, 12, 192) 576 ['conv2d_79[0][0]'] \n", " ormalization) \n", " \n", " activation_76 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_76[0][0]'] \n", " \n", " activation_79 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_79[0][0]'] \n", " \n", " block17_1_mixed (Concatenate) (None, 12, 12, 384) 0 ['activation_76[0][0]', \n", " 'activation_79[0][0]'] \n", " \n", " block17_1_conv (Conv2D) (None, 12, 12, 1088 418880 ['block17_1_mixed[0][0]'] \n", " ) \n", " \n", " custom_scale_layer_10 (CustomS (None, 12, 12, 1088 0 ['mixed_6a[0][0]', \n", " caleLayer) ) 'block17_1_conv[0][0]'] \n", " \n", " block17_1_ac (Activation) (None, 12, 12, 1088 0 ['custom_scale_layer_10[0][0]'] \n", " ) \n", " \n", " conv2d_81 (Conv2D) (None, 12, 12, 128) 139264 ['block17_1_ac[0][0]'] \n", " \n", " batch_normalization_81 (BatchN (None, 12, 12, 128) 384 ['conv2d_81[0][0]'] \n", " ormalization) \n", " \n", " activation_81 (Activation) (None, 12, 12, 128) 0 ['batch_normalization_81[0][0]'] \n", " \n", " conv2d_82 (Conv2D) (None, 12, 12, 160) 143360 ['activation_81[0][0]'] \n", " \n", " batch_normalization_82 (BatchN (None, 12, 12, 160) 480 ['conv2d_82[0][0]'] \n", " ormalization) \n", " \n", " activation_82 (Activation) (None, 12, 12, 160) 0 ['batch_normalization_82[0][0]'] \n", " \n", " conv2d_80 (Conv2D) (None, 12, 12, 192) 208896 ['block17_1_ac[0][0]'] \n", " \n", " conv2d_83 (Conv2D) (None, 12, 12, 192) 215040 ['activation_82[0][0]'] \n", " \n", " batch_normalization_80 (BatchN (None, 12, 12, 192) 576 ['conv2d_80[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_83 (BatchN (None, 12, 12, 192) 576 ['conv2d_83[0][0]'] \n", " ormalization) \n", " \n", " activation_80 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_80[0][0]'] \n", " \n", " activation_83 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_83[0][0]'] \n", " \n", " block17_2_mixed (Concatenate) (None, 12, 12, 384) 0 ['activation_80[0][0]', \n", " 'activation_83[0][0]'] \n", " \n", " block17_2_conv (Conv2D) (None, 12, 12, 1088 418880 ['block17_2_mixed[0][0]'] \n", " ) \n", " \n", " custom_scale_layer_11 (CustomS (None, 12, 12, 1088 0 ['block17_1_ac[0][0]', \n", " caleLayer) ) 'block17_2_conv[0][0]'] \n", " \n", " block17_2_ac (Activation) (None, 12, 12, 1088 0 ['custom_scale_layer_11[0][0]'] \n", " ) \n", " \n", " conv2d_85 (Conv2D) (None, 12, 12, 128) 139264 ['block17_2_ac[0][0]'] \n", " \n", " batch_normalization_85 (BatchN (None, 12, 12, 128) 384 ['conv2d_85[0][0]'] \n", " ormalization) \n", " \n", " activation_85 (Activation) (None, 12, 12, 128) 0 ['batch_normalization_85[0][0]'] \n", " \n", " conv2d_86 (Conv2D) (None, 12, 12, 160) 143360 ['activation_85[0][0]'] \n", " \n", " batch_normalization_86 (BatchN (None, 12, 12, 160) 480 ['conv2d_86[0][0]'] \n", " ormalization) \n", " \n", " activation_86 (Activation) (None, 12, 12, 160) 0 ['batch_normalization_86[0][0]'] \n", " \n", " conv2d_84 (Conv2D) (None, 12, 12, 192) 208896 ['block17_2_ac[0][0]'] \n", " \n", " conv2d_87 (Conv2D) (None, 12, 12, 192) 215040 ['activation_86[0][0]'] \n", " \n", " batch_normalization_84 (BatchN (None, 12, 12, 192) 576 ['conv2d_84[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_87 (BatchN (None, 12, 12, 192) 576 ['conv2d_87[0][0]'] \n", " ormalization) \n", " \n", " activation_84 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_84[0][0]'] \n", " \n", " activation_87 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_87[0][0]'] \n", " \n", " block17_3_mixed (Concatenate) (None, 12, 12, 384) 0 ['activation_84[0][0]', \n", " 'activation_87[0][0]'] \n", " \n", " block17_3_conv (Conv2D) (None, 12, 12, 1088 418880 ['block17_3_mixed[0][0]'] \n", " ) \n", " \n", " custom_scale_layer_12 (CustomS (None, 12, 12, 1088 0 ['block17_2_ac[0][0]', \n", " caleLayer) ) 'block17_3_conv[0][0]'] \n", " \n", " block17_3_ac (Activation) (None, 12, 12, 1088 0 ['custom_scale_layer_12[0][0]'] \n", " ) \n", " \n", " conv2d_89 (Conv2D) (None, 12, 12, 128) 139264 ['block17_3_ac[0][0]'] \n", " \n", " batch_normalization_89 (BatchN (None, 12, 12, 128) 384 ['conv2d_89[0][0]'] \n", " ormalization) \n", " \n", " activation_89 (Activation) (None, 12, 12, 128) 0 ['batch_normalization_89[0][0]'] \n", " \n", " conv2d_90 (Conv2D) (None, 12, 12, 160) 143360 ['activation_89[0][0]'] \n", " \n", " batch_normalization_90 (BatchN (None, 12, 12, 160) 480 ['conv2d_90[0][0]'] \n", " ormalization) \n", " \n", " activation_90 (Activation) (None, 12, 12, 160) 0 ['batch_normalization_90[0][0]'] \n", " \n", " conv2d_88 (Conv2D) (None, 12, 12, 192) 208896 ['block17_3_ac[0][0]'] \n", " \n", " conv2d_91 (Conv2D) (None, 12, 12, 192) 215040 ['activation_90[0][0]'] \n", " \n", " batch_normalization_88 (BatchN (None, 12, 12, 192) 576 ['conv2d_88[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_91 (BatchN (None, 12, 12, 192) 576 ['conv2d_91[0][0]'] \n", " ormalization) \n", " \n", " activation_88 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_88[0][0]'] \n", " \n", " activation_91 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_91[0][0]'] \n", " \n", " block17_4_mixed (Concatenate) (None, 12, 12, 384) 0 ['activation_88[0][0]', \n", " 'activation_91[0][0]'] \n", " \n", " block17_4_conv (Conv2D) (None, 12, 12, 1088 418880 ['block17_4_mixed[0][0]'] \n", " ) \n", " \n", " custom_scale_layer_13 (CustomS (None, 12, 12, 1088 0 ['block17_3_ac[0][0]', \n", " caleLayer) ) 'block17_4_conv[0][0]'] \n", " \n", " block17_4_ac (Activation) (None, 12, 12, 1088 0 ['custom_scale_layer_13[0][0]'] \n", " ) \n", " \n", " conv2d_93 (Conv2D) (None, 12, 12, 128) 139264 ['block17_4_ac[0][0]'] \n", " \n", " batch_normalization_93 (BatchN (None, 12, 12, 128) 384 ['conv2d_93[0][0]'] \n", " ormalization) \n", " \n", " activation_93 (Activation) (None, 12, 12, 128) 0 ['batch_normalization_93[0][0]'] \n", " \n", " conv2d_94 (Conv2D) (None, 12, 12, 160) 143360 ['activation_93[0][0]'] \n", " \n", " batch_normalization_94 (BatchN (None, 12, 12, 160) 480 ['conv2d_94[0][0]'] \n", " ormalization) \n", " \n", " activation_94 (Activation) (None, 12, 12, 160) 0 ['batch_normalization_94[0][0]'] \n", " \n", " conv2d_92 (Conv2D) (None, 12, 12, 192) 208896 ['block17_4_ac[0][0]'] \n", " \n", " conv2d_95 (Conv2D) (None, 12, 12, 192) 215040 ['activation_94[0][0]'] \n", " \n", " batch_normalization_92 (BatchN (None, 12, 12, 192) 576 ['conv2d_92[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_95 (BatchN (None, 12, 12, 192) 576 ['conv2d_95[0][0]'] \n", " ormalization) \n", " \n", " activation_92 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_92[0][0]'] \n", " \n", " activation_95 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_95[0][0]'] \n", " \n", " block17_5_mixed (Concatenate) (None, 12, 12, 384) 0 ['activation_92[0][0]', \n", " 'activation_95[0][0]'] \n", " \n", " block17_5_conv (Conv2D) (None, 12, 12, 1088 418880 ['block17_5_mixed[0][0]'] \n", " ) \n", " \n", " custom_scale_layer_14 (CustomS (None, 12, 12, 1088 0 ['block17_4_ac[0][0]', \n", " caleLayer) ) 'block17_5_conv[0][0]'] \n", " \n", " block17_5_ac (Activation) (None, 12, 12, 1088 0 ['custom_scale_layer_14[0][0]'] \n", " ) \n", " \n", " conv2d_97 (Conv2D) (None, 12, 12, 128) 139264 ['block17_5_ac[0][0]'] \n", " \n", " batch_normalization_97 (BatchN (None, 12, 12, 128) 384 ['conv2d_97[0][0]'] \n", " ormalization) \n", " \n", " activation_97 (Activation) (None, 12, 12, 128) 0 ['batch_normalization_97[0][0]'] \n", " \n", " conv2d_98 (Conv2D) (None, 12, 12, 160) 143360 ['activation_97[0][0]'] \n", " \n", " batch_normalization_98 (BatchN (None, 12, 12, 160) 480 ['conv2d_98[0][0]'] \n", " ormalization) \n", " \n", " activation_98 (Activation) (None, 12, 12, 160) 0 ['batch_normalization_98[0][0]'] \n", " \n", " conv2d_96 (Conv2D) (None, 12, 12, 192) 208896 ['block17_5_ac[0][0]'] \n", " \n", " conv2d_99 (Conv2D) (None, 12, 12, 192) 215040 ['activation_98[0][0]'] \n", " \n", " batch_normalization_96 (BatchN (None, 12, 12, 192) 576 ['conv2d_96[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_99 (BatchN (None, 12, 12, 192) 576 ['conv2d_99[0][0]'] \n", " ormalization) \n", " \n", " activation_96 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_96[0][0]'] \n", " \n", " activation_99 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_99[0][0]'] \n", " \n", " block17_6_mixed (Concatenate) (None, 12, 12, 384) 0 ['activation_96[0][0]', \n", " 'activation_99[0][0]'] \n", " \n", " block17_6_conv (Conv2D) (None, 12, 12, 1088 418880 ['block17_6_mixed[0][0]'] \n", " ) \n", " \n", " custom_scale_layer_15 (CustomS (None, 12, 12, 1088 0 ['block17_5_ac[0][0]', \n", " caleLayer) ) 'block17_6_conv[0][0]'] \n", " \n", " block17_6_ac (Activation) (None, 12, 12, 1088 0 ['custom_scale_layer_15[0][0]'] \n", " ) \n", " \n", " conv2d_101 (Conv2D) (None, 12, 12, 128) 139264 ['block17_6_ac[0][0]'] \n", " \n", " batch_normalization_101 (Batch (None, 12, 12, 128) 384 ['conv2d_101[0][0]'] \n", " Normalization) \n", " \n", " activation_101 (Activation) (None, 12, 12, 128) 0 ['batch_normalization_101[0][0]']\n", " \n", " conv2d_102 (Conv2D) (None, 12, 12, 160) 143360 ['activation_101[0][0]'] \n", " \n", " batch_normalization_102 (Batch (None, 12, 12, 160) 480 ['conv2d_102[0][0]'] \n", " Normalization) \n", " \n", " activation_102 (Activation) (None, 12, 12, 160) 0 ['batch_normalization_102[0][0]']\n", " \n", " conv2d_100 (Conv2D) (None, 12, 12, 192) 208896 ['block17_6_ac[0][0]'] \n", " \n", " conv2d_103 (Conv2D) (None, 12, 12, 192) 215040 ['activation_102[0][0]'] \n", " \n", " batch_normalization_100 (Batch (None, 12, 12, 192) 576 ['conv2d_100[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_103 (Batch (None, 12, 12, 192) 576 ['conv2d_103[0][0]'] \n", " Normalization) \n", " \n", " activation_100 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_100[0][0]']\n", " \n", " activation_103 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_103[0][0]']\n", " \n", " block17_7_mixed (Concatenate) (None, 12, 12, 384) 0 ['activation_100[0][0]', \n", " 'activation_103[0][0]'] \n", " \n", " block17_7_conv (Conv2D) (None, 12, 12, 1088 418880 ['block17_7_mixed[0][0]'] \n", " ) \n", " \n", " custom_scale_layer_16 (CustomS (None, 12, 12, 1088 0 ['block17_6_ac[0][0]', \n", " caleLayer) ) 'block17_7_conv[0][0]'] \n", " \n", " block17_7_ac (Activation) (None, 12, 12, 1088 0 ['custom_scale_layer_16[0][0]'] \n", " ) \n", " \n", " conv2d_105 (Conv2D) (None, 12, 12, 128) 139264 ['block17_7_ac[0][0]'] \n", " \n", " batch_normalization_105 (Batch (None, 12, 12, 128) 384 ['conv2d_105[0][0]'] \n", " Normalization) \n", " \n", " activation_105 (Activation) (None, 12, 12, 128) 0 ['batch_normalization_105[0][0]']\n", " \n", " conv2d_106 (Conv2D) (None, 12, 12, 160) 143360 ['activation_105[0][0]'] \n", " \n", " batch_normalization_106 (Batch (None, 12, 12, 160) 480 ['conv2d_106[0][0]'] \n", " Normalization) \n", " \n", " activation_106 (Activation) (None, 12, 12, 160) 0 ['batch_normalization_106[0][0]']\n", " \n", " conv2d_104 (Conv2D) (None, 12, 12, 192) 208896 ['block17_7_ac[0][0]'] \n", " \n", " conv2d_107 (Conv2D) (None, 12, 12, 192) 215040 ['activation_106[0][0]'] \n", " \n", " batch_normalization_104 (Batch (None, 12, 12, 192) 576 ['conv2d_104[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_107 (Batch (None, 12, 12, 192) 576 ['conv2d_107[0][0]'] \n", " Normalization) \n", " \n", " activation_104 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_104[0][0]']\n", " \n", " activation_107 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_107[0][0]']\n", " \n", " block17_8_mixed (Concatenate) (None, 12, 12, 384) 0 ['activation_104[0][0]', \n", " 'activation_107[0][0]'] \n", " \n", " block17_8_conv (Conv2D) (None, 12, 12, 1088 418880 ['block17_8_mixed[0][0]'] \n", " ) \n", " \n", " custom_scale_layer_17 (CustomS (None, 12, 12, 1088 0 ['block17_7_ac[0][0]', \n", " caleLayer) ) 'block17_8_conv[0][0]'] \n", " \n", " block17_8_ac (Activation) (None, 12, 12, 1088 0 ['custom_scale_layer_17[0][0]'] \n", " ) \n", " \n", " conv2d_109 (Conv2D) (None, 12, 12, 128) 139264 ['block17_8_ac[0][0]'] \n", " \n", " batch_normalization_109 (Batch (None, 12, 12, 128) 384 ['conv2d_109[0][0]'] \n", " Normalization) \n", " \n", " activation_109 (Activation) (None, 12, 12, 128) 0 ['batch_normalization_109[0][0]']\n", " \n", " conv2d_110 (Conv2D) (None, 12, 12, 160) 143360 ['activation_109[0][0]'] \n", " \n", " batch_normalization_110 (Batch (None, 12, 12, 160) 480 ['conv2d_110[0][0]'] \n", " Normalization) \n", " \n", " activation_110 (Activation) (None, 12, 12, 160) 0 ['batch_normalization_110[0][0]']\n", " \n", " conv2d_108 (Conv2D) (None, 12, 12, 192) 208896 ['block17_8_ac[0][0]'] \n", " \n", " conv2d_111 (Conv2D) (None, 12, 12, 192) 215040 ['activation_110[0][0]'] \n", " \n", " batch_normalization_108 (Batch (None, 12, 12, 192) 576 ['conv2d_108[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_111 (Batch (None, 12, 12, 192) 576 ['conv2d_111[0][0]'] \n", " Normalization) \n", " \n", " activation_108 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_108[0][0]']\n", " \n", " activation_111 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_111[0][0]']\n", " \n", " block17_9_mixed (Concatenate) (None, 12, 12, 384) 0 ['activation_108[0][0]', \n", " 'activation_111[0][0]'] \n", " \n", " block17_9_conv (Conv2D) (None, 12, 12, 1088 418880 ['block17_9_mixed[0][0]'] \n", " ) \n", " \n", " custom_scale_layer_18 (CustomS (None, 12, 12, 1088 0 ['block17_8_ac[0][0]', \n", " caleLayer) ) 'block17_9_conv[0][0]'] \n", " \n", " block17_9_ac (Activation) (None, 12, 12, 1088 0 ['custom_scale_layer_18[0][0]'] \n", " ) \n", " \n", " conv2d_113 (Conv2D) (None, 12, 12, 128) 139264 ['block17_9_ac[0][0]'] \n", " \n", " batch_normalization_113 (Batch (None, 12, 12, 128) 384 ['conv2d_113[0][0]'] \n", " Normalization) \n", " \n", " activation_113 (Activation) (None, 12, 12, 128) 0 ['batch_normalization_113[0][0]']\n", " \n", " conv2d_114 (Conv2D) (None, 12, 12, 160) 143360 ['activation_113[0][0]'] \n", " \n", " batch_normalization_114 (Batch (None, 12, 12, 160) 480 ['conv2d_114[0][0]'] \n", " Normalization) \n", " \n", " activation_114 (Activation) (None, 12, 12, 160) 0 ['batch_normalization_114[0][0]']\n", " \n", " conv2d_112 (Conv2D) (None, 12, 12, 192) 208896 ['block17_9_ac[0][0]'] \n", " \n", " conv2d_115 (Conv2D) (None, 12, 12, 192) 215040 ['activation_114[0][0]'] \n", " \n", " batch_normalization_112 (Batch (None, 12, 12, 192) 576 ['conv2d_112[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_115 (Batch (None, 12, 12, 192) 576 ['conv2d_115[0][0]'] \n", " Normalization) \n", " \n", " activation_112 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_112[0][0]']\n", " \n", " activation_115 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_115[0][0]']\n", " \n", " block17_10_mixed (Concatenate) (None, 12, 12, 384) 0 ['activation_112[0][0]', \n", " 'activation_115[0][0]'] \n", " \n", " block17_10_conv (Conv2D) (None, 12, 12, 1088 418880 ['block17_10_mixed[0][0]'] \n", " ) \n", " \n", " custom_scale_layer_19 (CustomS (None, 12, 12, 1088 0 ['block17_9_ac[0][0]', \n", " caleLayer) ) 'block17_10_conv[0][0]'] \n", " \n", " block17_10_ac (Activation) (None, 12, 12, 1088 0 ['custom_scale_layer_19[0][0]'] \n", " ) \n", " \n", " conv2d_117 (Conv2D) (None, 12, 12, 128) 139264 ['block17_10_ac[0][0]'] \n", " \n", " batch_normalization_117 (Batch (None, 12, 12, 128) 384 ['conv2d_117[0][0]'] \n", " Normalization) \n", " \n", " activation_117 (Activation) (None, 12, 12, 128) 0 ['batch_normalization_117[0][0]']\n", " \n", " conv2d_118 (Conv2D) (None, 12, 12, 160) 143360 ['activation_117[0][0]'] \n", " \n", " batch_normalization_118 (Batch (None, 12, 12, 160) 480 ['conv2d_118[0][0]'] \n", " Normalization) \n", " \n", " activation_118 (Activation) (None, 12, 12, 160) 0 ['batch_normalization_118[0][0]']\n", " \n", " conv2d_116 (Conv2D) (None, 12, 12, 192) 208896 ['block17_10_ac[0][0]'] \n", " \n", " conv2d_119 (Conv2D) (None, 12, 12, 192) 215040 ['activation_118[0][0]'] \n", " \n", " batch_normalization_116 (Batch (None, 12, 12, 192) 576 ['conv2d_116[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_119 (Batch (None, 12, 12, 192) 576 ['conv2d_119[0][0]'] \n", " Normalization) \n", " \n", " activation_116 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_116[0][0]']\n", " \n", " activation_119 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_119[0][0]']\n", " \n", " block17_11_mixed (Concatenate) (None, 12, 12, 384) 0 ['activation_116[0][0]', \n", " 'activation_119[0][0]'] \n", " \n", " block17_11_conv (Conv2D) (None, 12, 12, 1088 418880 ['block17_11_mixed[0][0]'] \n", " ) \n", " \n", " custom_scale_layer_20 (CustomS (None, 12, 12, 1088 0 ['block17_10_ac[0][0]', \n", " caleLayer) ) 'block17_11_conv[0][0]'] \n", " \n", " block17_11_ac (Activation) (None, 12, 12, 1088 0 ['custom_scale_layer_20[0][0]'] \n", " ) \n", " \n", " conv2d_121 (Conv2D) (None, 12, 12, 128) 139264 ['block17_11_ac[0][0]'] \n", " \n", " batch_normalization_121 (Batch (None, 12, 12, 128) 384 ['conv2d_121[0][0]'] \n", " Normalization) \n", " \n", " activation_121 (Activation) (None, 12, 12, 128) 0 ['batch_normalization_121[0][0]']\n", " \n", " conv2d_122 (Conv2D) (None, 12, 12, 160) 143360 ['activation_121[0][0]'] \n", " \n", " batch_normalization_122 (Batch (None, 12, 12, 160) 480 ['conv2d_122[0][0]'] \n", " Normalization) \n", " \n", " activation_122 (Activation) (None, 12, 12, 160) 0 ['batch_normalization_122[0][0]']\n", " \n", " conv2d_120 (Conv2D) (None, 12, 12, 192) 208896 ['block17_11_ac[0][0]'] \n", " \n", " conv2d_123 (Conv2D) (None, 12, 12, 192) 215040 ['activation_122[0][0]'] \n", " \n", " batch_normalization_120 (Batch (None, 12, 12, 192) 576 ['conv2d_120[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_123 (Batch (None, 12, 12, 192) 576 ['conv2d_123[0][0]'] \n", " Normalization) \n", " \n", " activation_120 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_120[0][0]']\n", " \n", " activation_123 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_123[0][0]']\n", " \n", " block17_12_mixed (Concatenate) (None, 12, 12, 384) 0 ['activation_120[0][0]', \n", " 'activation_123[0][0]'] \n", " \n", " block17_12_conv (Conv2D) (None, 12, 12, 1088 418880 ['block17_12_mixed[0][0]'] \n", " ) \n", " \n", " custom_scale_layer_21 (CustomS (None, 12, 12, 1088 0 ['block17_11_ac[0][0]', \n", " caleLayer) ) 'block17_12_conv[0][0]'] \n", " \n", " block17_12_ac (Activation) (None, 12, 12, 1088 0 ['custom_scale_layer_21[0][0]'] \n", " ) \n", " \n", " conv2d_125 (Conv2D) (None, 12, 12, 128) 139264 ['block17_12_ac[0][0]'] \n", " \n", " batch_normalization_125 (Batch (None, 12, 12, 128) 384 ['conv2d_125[0][0]'] \n", " Normalization) \n", " \n", " activation_125 (Activation) (None, 12, 12, 128) 0 ['batch_normalization_125[0][0]']\n", " \n", " conv2d_126 (Conv2D) (None, 12, 12, 160) 143360 ['activation_125[0][0]'] \n", " \n", " batch_normalization_126 (Batch (None, 12, 12, 160) 480 ['conv2d_126[0][0]'] \n", " Normalization) \n", " \n", " activation_126 (Activation) (None, 12, 12, 160) 0 ['batch_normalization_126[0][0]']\n", " \n", " conv2d_124 (Conv2D) (None, 12, 12, 192) 208896 ['block17_12_ac[0][0]'] \n", " \n", " conv2d_127 (Conv2D) (None, 12, 12, 192) 215040 ['activation_126[0][0]'] \n", " \n", " batch_normalization_124 (Batch (None, 12, 12, 192) 576 ['conv2d_124[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_127 (Batch (None, 12, 12, 192) 576 ['conv2d_127[0][0]'] \n", " Normalization) \n", " \n", " activation_124 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_124[0][0]']\n", " \n", " activation_127 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_127[0][0]']\n", " \n", " block17_13_mixed (Concatenate) (None, 12, 12, 384) 0 ['activation_124[0][0]', \n", " 'activation_127[0][0]'] \n", " \n", " block17_13_conv (Conv2D) (None, 12, 12, 1088 418880 ['block17_13_mixed[0][0]'] \n", " ) \n", " \n", " custom_scale_layer_22 (CustomS (None, 12, 12, 1088 0 ['block17_12_ac[0][0]', \n", " caleLayer) ) 'block17_13_conv[0][0]'] \n", " \n", " block17_13_ac (Activation) (None, 12, 12, 1088 0 ['custom_scale_layer_22[0][0]'] \n", " ) \n", " \n", " conv2d_129 (Conv2D) (None, 12, 12, 128) 139264 ['block17_13_ac[0][0]'] \n", " \n", " batch_normalization_129 (Batch (None, 12, 12, 128) 384 ['conv2d_129[0][0]'] \n", " Normalization) \n", " \n", " activation_129 (Activation) (None, 12, 12, 128) 0 ['batch_normalization_129[0][0]']\n", " \n", " conv2d_130 (Conv2D) (None, 12, 12, 160) 143360 ['activation_129[0][0]'] \n", " \n", " batch_normalization_130 (Batch (None, 12, 12, 160) 480 ['conv2d_130[0][0]'] \n", " Normalization) \n", " \n", " activation_130 (Activation) (None, 12, 12, 160) 0 ['batch_normalization_130[0][0]']\n", " \n", " conv2d_128 (Conv2D) (None, 12, 12, 192) 208896 ['block17_13_ac[0][0]'] \n", " \n", " conv2d_131 (Conv2D) (None, 12, 12, 192) 215040 ['activation_130[0][0]'] \n", " \n", " batch_normalization_128 (Batch (None, 12, 12, 192) 576 ['conv2d_128[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_131 (Batch (None, 12, 12, 192) 576 ['conv2d_131[0][0]'] \n", " Normalization) \n", " \n", " activation_128 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_128[0][0]']\n", " \n", " activation_131 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_131[0][0]']\n", " \n", " block17_14_mixed (Concatenate) (None, 12, 12, 384) 0 ['activation_128[0][0]', \n", " 'activation_131[0][0]'] \n", " \n", " block17_14_conv (Conv2D) (None, 12, 12, 1088 418880 ['block17_14_mixed[0][0]'] \n", " ) \n", " \n", " custom_scale_layer_23 (CustomS (None, 12, 12, 1088 0 ['block17_13_ac[0][0]', \n", " caleLayer) ) 'block17_14_conv[0][0]'] \n", " \n", " block17_14_ac (Activation) (None, 12, 12, 1088 0 ['custom_scale_layer_23[0][0]'] \n", " ) \n", " \n", " conv2d_133 (Conv2D) (None, 12, 12, 128) 139264 ['block17_14_ac[0][0]'] \n", " \n", " batch_normalization_133 (Batch (None, 12, 12, 128) 384 ['conv2d_133[0][0]'] \n", " Normalization) \n", " \n", " activation_133 (Activation) (None, 12, 12, 128) 0 ['batch_normalization_133[0][0]']\n", " \n", " conv2d_134 (Conv2D) (None, 12, 12, 160) 143360 ['activation_133[0][0]'] \n", " \n", " batch_normalization_134 (Batch (None, 12, 12, 160) 480 ['conv2d_134[0][0]'] \n", " Normalization) \n", " \n", " activation_134 (Activation) (None, 12, 12, 160) 0 ['batch_normalization_134[0][0]']\n", " \n", " conv2d_132 (Conv2D) (None, 12, 12, 192) 208896 ['block17_14_ac[0][0]'] \n", " \n", " conv2d_135 (Conv2D) (None, 12, 12, 192) 215040 ['activation_134[0][0]'] \n", " \n", " batch_normalization_132 (Batch (None, 12, 12, 192) 576 ['conv2d_132[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_135 (Batch (None, 12, 12, 192) 576 ['conv2d_135[0][0]'] \n", " Normalization) \n", " \n", " activation_132 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_132[0][0]']\n", " \n", " activation_135 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_135[0][0]']\n", " \n", " block17_15_mixed (Concatenate) (None, 12, 12, 384) 0 ['activation_132[0][0]', \n", " 'activation_135[0][0]'] \n", " \n", " block17_15_conv (Conv2D) (None, 12, 12, 1088 418880 ['block17_15_mixed[0][0]'] \n", " ) \n", " \n", " custom_scale_layer_24 (CustomS (None, 12, 12, 1088 0 ['block17_14_ac[0][0]', \n", " caleLayer) ) 'block17_15_conv[0][0]'] \n", " \n", " block17_15_ac (Activation) (None, 12, 12, 1088 0 ['custom_scale_layer_24[0][0]'] \n", " ) \n", " \n", " conv2d_137 (Conv2D) (None, 12, 12, 128) 139264 ['block17_15_ac[0][0]'] \n", " \n", " batch_normalization_137 (Batch (None, 12, 12, 128) 384 ['conv2d_137[0][0]'] \n", " Normalization) \n", " \n", " activation_137 (Activation) (None, 12, 12, 128) 0 ['batch_normalization_137[0][0]']\n", " \n", " conv2d_138 (Conv2D) (None, 12, 12, 160) 143360 ['activation_137[0][0]'] \n", " \n", " batch_normalization_138 (Batch (None, 12, 12, 160) 480 ['conv2d_138[0][0]'] \n", " Normalization) \n", " \n", " activation_138 (Activation) (None, 12, 12, 160) 0 ['batch_normalization_138[0][0]']\n", " \n", " conv2d_136 (Conv2D) (None, 12, 12, 192) 208896 ['block17_15_ac[0][0]'] \n", " \n", " conv2d_139 (Conv2D) (None, 12, 12, 192) 215040 ['activation_138[0][0]'] \n", " \n", " batch_normalization_136 (Batch (None, 12, 12, 192) 576 ['conv2d_136[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_139 (Batch (None, 12, 12, 192) 576 ['conv2d_139[0][0]'] \n", " Normalization) \n", " \n", " activation_136 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_136[0][0]']\n", " \n", " activation_139 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_139[0][0]']\n", " \n", " block17_16_mixed (Concatenate) (None, 12, 12, 384) 0 ['activation_136[0][0]', \n", " 'activation_139[0][0]'] \n", " \n", " block17_16_conv (Conv2D) (None, 12, 12, 1088 418880 ['block17_16_mixed[0][0]'] \n", " ) \n", " \n", " custom_scale_layer_25 (CustomS (None, 12, 12, 1088 0 ['block17_15_ac[0][0]', \n", " caleLayer) ) 'block17_16_conv[0][0]'] \n", " \n", " block17_16_ac (Activation) (None, 12, 12, 1088 0 ['custom_scale_layer_25[0][0]'] \n", " ) \n", " \n", " conv2d_141 (Conv2D) (None, 12, 12, 128) 139264 ['block17_16_ac[0][0]'] \n", " \n", " batch_normalization_141 (Batch (None, 12, 12, 128) 384 ['conv2d_141[0][0]'] \n", " Normalization) \n", " \n", " activation_141 (Activation) (None, 12, 12, 128) 0 ['batch_normalization_141[0][0]']\n", " \n", " conv2d_142 (Conv2D) (None, 12, 12, 160) 143360 ['activation_141[0][0]'] \n", " \n", " batch_normalization_142 (Batch (None, 12, 12, 160) 480 ['conv2d_142[0][0]'] \n", " Normalization) \n", " \n", " activation_142 (Activation) (None, 12, 12, 160) 0 ['batch_normalization_142[0][0]']\n", " \n", " conv2d_140 (Conv2D) (None, 12, 12, 192) 208896 ['block17_16_ac[0][0]'] \n", " \n", " conv2d_143 (Conv2D) (None, 12, 12, 192) 215040 ['activation_142[0][0]'] \n", " \n", " batch_normalization_140 (Batch (None, 12, 12, 192) 576 ['conv2d_140[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_143 (Batch (None, 12, 12, 192) 576 ['conv2d_143[0][0]'] \n", " Normalization) \n", " \n", " activation_140 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_140[0][0]']\n", " \n", " activation_143 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_143[0][0]']\n", " \n", " block17_17_mixed (Concatenate) (None, 12, 12, 384) 0 ['activation_140[0][0]', \n", " 'activation_143[0][0]'] \n", " \n", " block17_17_conv (Conv2D) (None, 12, 12, 1088 418880 ['block17_17_mixed[0][0]'] \n", " ) \n", " \n", " custom_scale_layer_26 (CustomS (None, 12, 12, 1088 0 ['block17_16_ac[0][0]', \n", " caleLayer) ) 'block17_17_conv[0][0]'] \n", " \n", " block17_17_ac (Activation) (None, 12, 12, 1088 0 ['custom_scale_layer_26[0][0]'] \n", " ) \n", " \n", " conv2d_145 (Conv2D) (None, 12, 12, 128) 139264 ['block17_17_ac[0][0]'] \n", " \n", " batch_normalization_145 (Batch (None, 12, 12, 128) 384 ['conv2d_145[0][0]'] \n", " Normalization) \n", " \n", " activation_145 (Activation) (None, 12, 12, 128) 0 ['batch_normalization_145[0][0]']\n", " \n", " conv2d_146 (Conv2D) (None, 12, 12, 160) 143360 ['activation_145[0][0]'] \n", " \n", " batch_normalization_146 (Batch (None, 12, 12, 160) 480 ['conv2d_146[0][0]'] \n", " Normalization) \n", " \n", " activation_146 (Activation) (None, 12, 12, 160) 0 ['batch_normalization_146[0][0]']\n", " \n", " conv2d_144 (Conv2D) (None, 12, 12, 192) 208896 ['block17_17_ac[0][0]'] \n", " \n", " conv2d_147 (Conv2D) (None, 12, 12, 192) 215040 ['activation_146[0][0]'] \n", " \n", " batch_normalization_144 (Batch (None, 12, 12, 192) 576 ['conv2d_144[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_147 (Batch (None, 12, 12, 192) 576 ['conv2d_147[0][0]'] \n", " Normalization) \n", " \n", " activation_144 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_144[0][0]']\n", " \n", " activation_147 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_147[0][0]']\n", " \n", " block17_18_mixed (Concatenate) (None, 12, 12, 384) 0 ['activation_144[0][0]', \n", " 'activation_147[0][0]'] \n", " \n", " block17_18_conv (Conv2D) (None, 12, 12, 1088 418880 ['block17_18_mixed[0][0]'] \n", " ) \n", " \n", " custom_scale_layer_27 (CustomS (None, 12, 12, 1088 0 ['block17_17_ac[0][0]', \n", " caleLayer) ) 'block17_18_conv[0][0]'] \n", " \n", " block17_18_ac (Activation) (None, 12, 12, 1088 0 ['custom_scale_layer_27[0][0]'] \n", " ) \n", " \n", " conv2d_149 (Conv2D) (None, 12, 12, 128) 139264 ['block17_18_ac[0][0]'] \n", " \n", " batch_normalization_149 (Batch (None, 12, 12, 128) 384 ['conv2d_149[0][0]'] \n", " Normalization) \n", " \n", " activation_149 (Activation) (None, 12, 12, 128) 0 ['batch_normalization_149[0][0]']\n", " \n", " conv2d_150 (Conv2D) (None, 12, 12, 160) 143360 ['activation_149[0][0]'] \n", " \n", " batch_normalization_150 (Batch (None, 12, 12, 160) 480 ['conv2d_150[0][0]'] \n", " Normalization) \n", " \n", " activation_150 (Activation) (None, 12, 12, 160) 0 ['batch_normalization_150[0][0]']\n", " \n", " conv2d_148 (Conv2D) (None, 12, 12, 192) 208896 ['block17_18_ac[0][0]'] \n", " \n", " conv2d_151 (Conv2D) (None, 12, 12, 192) 215040 ['activation_150[0][0]'] \n", " \n", " batch_normalization_148 (Batch (None, 12, 12, 192) 576 ['conv2d_148[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_151 (Batch (None, 12, 12, 192) 576 ['conv2d_151[0][0]'] \n", " Normalization) \n", " \n", " activation_148 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_148[0][0]']\n", " \n", " activation_151 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_151[0][0]']\n", " \n", " block17_19_mixed (Concatenate) (None, 12, 12, 384) 0 ['activation_148[0][0]', \n", " 'activation_151[0][0]'] \n", " \n", " block17_19_conv (Conv2D) (None, 12, 12, 1088 418880 ['block17_19_mixed[0][0]'] \n", " ) \n", " \n", " custom_scale_layer_28 (CustomS (None, 12, 12, 1088 0 ['block17_18_ac[0][0]', \n", " caleLayer) ) 'block17_19_conv[0][0]'] \n", " \n", " block17_19_ac (Activation) (None, 12, 12, 1088 0 ['custom_scale_layer_28[0][0]'] \n", " ) \n", " \n", " conv2d_153 (Conv2D) (None, 12, 12, 128) 139264 ['block17_19_ac[0][0]'] \n", " \n", " batch_normalization_153 (Batch (None, 12, 12, 128) 384 ['conv2d_153[0][0]'] \n", " Normalization) \n", " \n", " activation_153 (Activation) (None, 12, 12, 128) 0 ['batch_normalization_153[0][0]']\n", " \n", " conv2d_154 (Conv2D) (None, 12, 12, 160) 143360 ['activation_153[0][0]'] \n", " \n", " batch_normalization_154 (Batch (None, 12, 12, 160) 480 ['conv2d_154[0][0]'] \n", " Normalization) \n", " \n", " activation_154 (Activation) (None, 12, 12, 160) 0 ['batch_normalization_154[0][0]']\n", " \n", " conv2d_152 (Conv2D) (None, 12, 12, 192) 208896 ['block17_19_ac[0][0]'] \n", " \n", " conv2d_155 (Conv2D) (None, 12, 12, 192) 215040 ['activation_154[0][0]'] \n", " \n", " batch_normalization_152 (Batch (None, 12, 12, 192) 576 ['conv2d_152[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_155 (Batch (None, 12, 12, 192) 576 ['conv2d_155[0][0]'] \n", " Normalization) \n", " \n", " activation_152 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_152[0][0]']\n", " \n", " activation_155 (Activation) (None, 12, 12, 192) 0 ['batch_normalization_155[0][0]']\n", " \n", " block17_20_mixed (Concatenate) (None, 12, 12, 384) 0 ['activation_152[0][0]', \n", " 'activation_155[0][0]'] \n", " \n", " block17_20_conv (Conv2D) (None, 12, 12, 1088 418880 ['block17_20_mixed[0][0]'] \n", " ) \n", " \n", " custom_scale_layer_29 (CustomS (None, 12, 12, 1088 0 ['block17_19_ac[0][0]', \n", " caleLayer) ) 'block17_20_conv[0][0]'] \n", " \n", " block17_20_ac (Activation) (None, 12, 12, 1088 0 ['custom_scale_layer_29[0][0]'] \n", " ) \n", " \n", " conv2d_160 (Conv2D) (None, 12, 12, 256) 278528 ['block17_20_ac[0][0]'] \n", " \n", " batch_normalization_160 (Batch (None, 12, 12, 256) 768 ['conv2d_160[0][0]'] \n", " Normalization) \n", " \n", " activation_160 (Activation) (None, 12, 12, 256) 0 ['batch_normalization_160[0][0]']\n", " \n", " conv2d_156 (Conv2D) (None, 12, 12, 256) 278528 ['block17_20_ac[0][0]'] \n", " \n", " conv2d_158 (Conv2D) (None, 12, 12, 256) 278528 ['block17_20_ac[0][0]'] \n", " \n", " conv2d_161 (Conv2D) (None, 12, 12, 288) 663552 ['activation_160[0][0]'] \n", " \n", " batch_normalization_156 (Batch (None, 12, 12, 256) 768 ['conv2d_156[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_158 (Batch (None, 12, 12, 256) 768 ['conv2d_158[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_161 (Batch (None, 12, 12, 288) 864 ['conv2d_161[0][0]'] \n", " Normalization) \n", " \n", " activation_156 (Activation) (None, 12, 12, 256) 0 ['batch_normalization_156[0][0]']\n", " \n", " activation_158 (Activation) (None, 12, 12, 256) 0 ['batch_normalization_158[0][0]']\n", " \n", " activation_161 (Activation) (None, 12, 12, 288) 0 ['batch_normalization_161[0][0]']\n", " \n", " conv2d_157 (Conv2D) (None, 5, 5, 384) 884736 ['activation_156[0][0]'] \n", " \n", " conv2d_159 (Conv2D) (None, 5, 5, 288) 663552 ['activation_158[0][0]'] \n", " \n", " conv2d_162 (Conv2D) (None, 5, 5, 320) 829440 ['activation_161[0][0]'] \n", " \n", " batch_normalization_157 (Batch (None, 5, 5, 384) 1152 ['conv2d_157[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_159 (Batch (None, 5, 5, 288) 864 ['conv2d_159[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_162 (Batch (None, 5, 5, 320) 960 ['conv2d_162[0][0]'] \n", " Normalization) \n", " \n", " activation_157 (Activation) (None, 5, 5, 384) 0 ['batch_normalization_157[0][0]']\n", " \n", " activation_159 (Activation) (None, 5, 5, 288) 0 ['batch_normalization_159[0][0]']\n", " \n", " activation_162 (Activation) (None, 5, 5, 320) 0 ['batch_normalization_162[0][0]']\n", " \n", " max_pooling2d_3 (MaxPooling2D) (None, 5, 5, 1088) 0 ['block17_20_ac[0][0]'] \n", " \n", " mixed_7a (Concatenate) (None, 5, 5, 2080) 0 ['activation_157[0][0]', \n", " 'activation_159[0][0]', \n", " 'activation_162[0][0]', \n", " 'max_pooling2d_3[0][0]'] \n", " \n", " conv2d_164 (Conv2D) (None, 5, 5, 192) 399360 ['mixed_7a[0][0]'] \n", " \n", " batch_normalization_164 (Batch (None, 5, 5, 192) 576 ['conv2d_164[0][0]'] \n", " Normalization) \n", " \n", " activation_164 (Activation) (None, 5, 5, 192) 0 ['batch_normalization_164[0][0]']\n", " \n", " conv2d_165 (Conv2D) (None, 5, 5, 224) 129024 ['activation_164[0][0]'] \n", " \n", " batch_normalization_165 (Batch (None, 5, 5, 224) 672 ['conv2d_165[0][0]'] \n", " Normalization) \n", " \n", " activation_165 (Activation) (None, 5, 5, 224) 0 ['batch_normalization_165[0][0]']\n", " \n", " conv2d_163 (Conv2D) (None, 5, 5, 192) 399360 ['mixed_7a[0][0]'] \n", " \n", " conv2d_166 (Conv2D) (None, 5, 5, 256) 172032 ['activation_165[0][0]'] \n", " \n", " batch_normalization_163 (Batch (None, 5, 5, 192) 576 ['conv2d_163[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_166 (Batch (None, 5, 5, 256) 768 ['conv2d_166[0][0]'] \n", " Normalization) \n", " \n", " activation_163 (Activation) (None, 5, 5, 192) 0 ['batch_normalization_163[0][0]']\n", " \n", " activation_166 (Activation) (None, 5, 5, 256) 0 ['batch_normalization_166[0][0]']\n", " \n", " block8_1_mixed (Concatenate) (None, 5, 5, 448) 0 ['activation_163[0][0]', \n", " 'activation_166[0][0]'] \n", " \n", " block8_1_conv (Conv2D) (None, 5, 5, 2080) 933920 ['block8_1_mixed[0][0]'] \n", " \n", " custom_scale_layer_30 (CustomS (None, 5, 5, 2080) 0 ['mixed_7a[0][0]', \n", " caleLayer) 'block8_1_conv[0][0]'] \n", " \n", " block8_1_ac (Activation) (None, 5, 5, 2080) 0 ['custom_scale_layer_30[0][0]'] \n", " \n", " conv2d_168 (Conv2D) (None, 5, 5, 192) 399360 ['block8_1_ac[0][0]'] \n", " \n", " batch_normalization_168 (Batch (None, 5, 5, 192) 576 ['conv2d_168[0][0]'] \n", " Normalization) \n", " \n", " activation_168 (Activation) (None, 5, 5, 192) 0 ['batch_normalization_168[0][0]']\n", " \n", " conv2d_169 (Conv2D) (None, 5, 5, 224) 129024 ['activation_168[0][0]'] \n", " \n", " batch_normalization_169 (Batch (None, 5, 5, 224) 672 ['conv2d_169[0][0]'] \n", " Normalization) \n", " \n", " activation_169 (Activation) (None, 5, 5, 224) 0 ['batch_normalization_169[0][0]']\n", " \n", " conv2d_167 (Conv2D) (None, 5, 5, 192) 399360 ['block8_1_ac[0][0]'] \n", " \n", " conv2d_170 (Conv2D) (None, 5, 5, 256) 172032 ['activation_169[0][0]'] \n", " \n", " batch_normalization_167 (Batch (None, 5, 5, 192) 576 ['conv2d_167[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_170 (Batch (None, 5, 5, 256) 768 ['conv2d_170[0][0]'] \n", " Normalization) \n", " \n", " activation_167 (Activation) (None, 5, 5, 192) 0 ['batch_normalization_167[0][0]']\n", " \n", " activation_170 (Activation) (None, 5, 5, 256) 0 ['batch_normalization_170[0][0]']\n", " \n", " block8_2_mixed (Concatenate) (None, 5, 5, 448) 0 ['activation_167[0][0]', \n", " 'activation_170[0][0]'] \n", " \n", " block8_2_conv (Conv2D) (None, 5, 5, 2080) 933920 ['block8_2_mixed[0][0]'] \n", " \n", " custom_scale_layer_31 (CustomS (None, 5, 5, 2080) 0 ['block8_1_ac[0][0]', \n", " caleLayer) 'block8_2_conv[0][0]'] \n", " \n", " block8_2_ac (Activation) (None, 5, 5, 2080) 0 ['custom_scale_layer_31[0][0]'] \n", " \n", " conv2d_172 (Conv2D) (None, 5, 5, 192) 399360 ['block8_2_ac[0][0]'] \n", " \n", " batch_normalization_172 (Batch (None, 5, 5, 192) 576 ['conv2d_172[0][0]'] \n", " Normalization) \n", " \n", " activation_172 (Activation) (None, 5, 5, 192) 0 ['batch_normalization_172[0][0]']\n", " \n", " conv2d_173 (Conv2D) (None, 5, 5, 224) 129024 ['activation_172[0][0]'] \n", " \n", " batch_normalization_173 (Batch (None, 5, 5, 224) 672 ['conv2d_173[0][0]'] \n", " Normalization) \n", " \n", " activation_173 (Activation) (None, 5, 5, 224) 0 ['batch_normalization_173[0][0]']\n", " \n", " conv2d_171 (Conv2D) (None, 5, 5, 192) 399360 ['block8_2_ac[0][0]'] \n", " \n", " conv2d_174 (Conv2D) (None, 5, 5, 256) 172032 ['activation_173[0][0]'] \n", " \n", " batch_normalization_171 (Batch (None, 5, 5, 192) 576 ['conv2d_171[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_174 (Batch (None, 5, 5, 256) 768 ['conv2d_174[0][0]'] \n", " Normalization) \n", " \n", " activation_171 (Activation) (None, 5, 5, 192) 0 ['batch_normalization_171[0][0]']\n", " \n", " activation_174 (Activation) (None, 5, 5, 256) 0 ['batch_normalization_174[0][0]']\n", " \n", " block8_3_mixed (Concatenate) (None, 5, 5, 448) 0 ['activation_171[0][0]', \n", " 'activation_174[0][0]'] \n", " \n", " block8_3_conv (Conv2D) (None, 5, 5, 2080) 933920 ['block8_3_mixed[0][0]'] \n", " \n", " custom_scale_layer_32 (CustomS (None, 5, 5, 2080) 0 ['block8_2_ac[0][0]', \n", " caleLayer) 'block8_3_conv[0][0]'] \n", " \n", " block8_3_ac (Activation) (None, 5, 5, 2080) 0 ['custom_scale_layer_32[0][0]'] \n", " \n", " conv2d_176 (Conv2D) (None, 5, 5, 192) 399360 ['block8_3_ac[0][0]'] \n", " \n", " batch_normalization_176 (Batch (None, 5, 5, 192) 576 ['conv2d_176[0][0]'] \n", " Normalization) \n", " \n", " activation_176 (Activation) (None, 5, 5, 192) 0 ['batch_normalization_176[0][0]']\n", " \n", " conv2d_177 (Conv2D) (None, 5, 5, 224) 129024 ['activation_176[0][0]'] \n", " \n", " batch_normalization_177 (Batch (None, 5, 5, 224) 672 ['conv2d_177[0][0]'] \n", " Normalization) \n", " \n", " activation_177 (Activation) (None, 5, 5, 224) 0 ['batch_normalization_177[0][0]']\n", " \n", " conv2d_175 (Conv2D) (None, 5, 5, 192) 399360 ['block8_3_ac[0][0]'] \n", " \n", " conv2d_178 (Conv2D) (None, 5, 5, 256) 172032 ['activation_177[0][0]'] \n", " \n", " batch_normalization_175 (Batch (None, 5, 5, 192) 576 ['conv2d_175[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_178 (Batch (None, 5, 5, 256) 768 ['conv2d_178[0][0]'] \n", " Normalization) \n", " \n", " activation_175 (Activation) (None, 5, 5, 192) 0 ['batch_normalization_175[0][0]']\n", " \n", " activation_178 (Activation) (None, 5, 5, 256) 0 ['batch_normalization_178[0][0]']\n", " \n", " block8_4_mixed (Concatenate) (None, 5, 5, 448) 0 ['activation_175[0][0]', \n", " 'activation_178[0][0]'] \n", " \n", " block8_4_conv (Conv2D) (None, 5, 5, 2080) 933920 ['block8_4_mixed[0][0]'] \n", " \n", " custom_scale_layer_33 (CustomS (None, 5, 5, 2080) 0 ['block8_3_ac[0][0]', \n", " caleLayer) 'block8_4_conv[0][0]'] \n", " \n", " block8_4_ac (Activation) (None, 5, 5, 2080) 0 ['custom_scale_layer_33[0][0]'] \n", " \n", " conv2d_180 (Conv2D) (None, 5, 5, 192) 399360 ['block8_4_ac[0][0]'] \n", " \n", " batch_normalization_180 (Batch (None, 5, 5, 192) 576 ['conv2d_180[0][0]'] \n", " Normalization) \n", " \n", " activation_180 (Activation) (None, 5, 5, 192) 0 ['batch_normalization_180[0][0]']\n", " \n", " conv2d_181 (Conv2D) (None, 5, 5, 224) 129024 ['activation_180[0][0]'] \n", " \n", " batch_normalization_181 (Batch (None, 5, 5, 224) 672 ['conv2d_181[0][0]'] \n", " Normalization) \n", " \n", " activation_181 (Activation) (None, 5, 5, 224) 0 ['batch_normalization_181[0][0]']\n", " \n", " conv2d_179 (Conv2D) (None, 5, 5, 192) 399360 ['block8_4_ac[0][0]'] \n", " \n", " conv2d_182 (Conv2D) (None, 5, 5, 256) 172032 ['activation_181[0][0]'] \n", " \n", " batch_normalization_179 (Batch (None, 5, 5, 192) 576 ['conv2d_179[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_182 (Batch (None, 5, 5, 256) 768 ['conv2d_182[0][0]'] \n", " Normalization) \n", " \n", " activation_179 (Activation) (None, 5, 5, 192) 0 ['batch_normalization_179[0][0]']\n", " \n", " activation_182 (Activation) (None, 5, 5, 256) 0 ['batch_normalization_182[0][0]']\n", " \n", " block8_5_mixed (Concatenate) (None, 5, 5, 448) 0 ['activation_179[0][0]', \n", " 'activation_182[0][0]'] \n", " \n", " block8_5_conv (Conv2D) (None, 5, 5, 2080) 933920 ['block8_5_mixed[0][0]'] \n", " \n", " custom_scale_layer_34 (CustomS (None, 5, 5, 2080) 0 ['block8_4_ac[0][0]', \n", " caleLayer) 'block8_5_conv[0][0]'] \n", " \n", " block8_5_ac (Activation) (None, 5, 5, 2080) 0 ['custom_scale_layer_34[0][0]'] \n", " \n", " conv2d_184 (Conv2D) (None, 5, 5, 192) 399360 ['block8_5_ac[0][0]'] \n", " \n", " batch_normalization_184 (Batch (None, 5, 5, 192) 576 ['conv2d_184[0][0]'] \n", " Normalization) \n", " \n", " activation_184 (Activation) (None, 5, 5, 192) 0 ['batch_normalization_184[0][0]']\n", " \n", " conv2d_185 (Conv2D) (None, 5, 5, 224) 129024 ['activation_184[0][0]'] \n", " \n", " batch_normalization_185 (Batch (None, 5, 5, 224) 672 ['conv2d_185[0][0]'] \n", " Normalization) \n", " \n", " activation_185 (Activation) (None, 5, 5, 224) 0 ['batch_normalization_185[0][0]']\n", " \n", " conv2d_183 (Conv2D) (None, 5, 5, 192) 399360 ['block8_5_ac[0][0]'] \n", " \n", " conv2d_186 (Conv2D) (None, 5, 5, 256) 172032 ['activation_185[0][0]'] \n", " \n", " batch_normalization_183 (Batch (None, 5, 5, 192) 576 ['conv2d_183[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_186 (Batch (None, 5, 5, 256) 768 ['conv2d_186[0][0]'] \n", " Normalization) \n", " \n", " activation_183 (Activation) (None, 5, 5, 192) 0 ['batch_normalization_183[0][0]']\n", " \n", " activation_186 (Activation) (None, 5, 5, 256) 0 ['batch_normalization_186[0][0]']\n", " \n", " block8_6_mixed (Concatenate) (None, 5, 5, 448) 0 ['activation_183[0][0]', \n", " 'activation_186[0][0]'] \n", " \n", " block8_6_conv (Conv2D) (None, 5, 5, 2080) 933920 ['block8_6_mixed[0][0]'] \n", " \n", " custom_scale_layer_35 (CustomS (None, 5, 5, 2080) 0 ['block8_5_ac[0][0]', \n", " caleLayer) 'block8_6_conv[0][0]'] \n", " \n", " block8_6_ac (Activation) (None, 5, 5, 2080) 0 ['custom_scale_layer_35[0][0]'] \n", " \n", " conv2d_188 (Conv2D) (None, 5, 5, 192) 399360 ['block8_6_ac[0][0]'] \n", " \n", " batch_normalization_188 (Batch (None, 5, 5, 192) 576 ['conv2d_188[0][0]'] \n", " Normalization) \n", " \n", " activation_188 (Activation) (None, 5, 5, 192) 0 ['batch_normalization_188[0][0]']\n", " \n", " conv2d_189 (Conv2D) (None, 5, 5, 224) 129024 ['activation_188[0][0]'] \n", " \n", " batch_normalization_189 (Batch (None, 5, 5, 224) 672 ['conv2d_189[0][0]'] \n", " Normalization) \n", " \n", " activation_189 (Activation) (None, 5, 5, 224) 0 ['batch_normalization_189[0][0]']\n", " \n", " conv2d_187 (Conv2D) (None, 5, 5, 192) 399360 ['block8_6_ac[0][0]'] \n", " \n", " conv2d_190 (Conv2D) (None, 5, 5, 256) 172032 ['activation_189[0][0]'] \n", " \n", " batch_normalization_187 (Batch (None, 5, 5, 192) 576 ['conv2d_187[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_190 (Batch (None, 5, 5, 256) 768 ['conv2d_190[0][0]'] \n", " Normalization) \n", " \n", " activation_187 (Activation) (None, 5, 5, 192) 0 ['batch_normalization_187[0][0]']\n", " \n", " activation_190 (Activation) (None, 5, 5, 256) 0 ['batch_normalization_190[0][0]']\n", " \n", " block8_7_mixed (Concatenate) (None, 5, 5, 448) 0 ['activation_187[0][0]', \n", " 'activation_190[0][0]'] \n", " \n", " block8_7_conv (Conv2D) (None, 5, 5, 2080) 933920 ['block8_7_mixed[0][0]'] \n", " \n", " custom_scale_layer_36 (CustomS (None, 5, 5, 2080) 0 ['block8_6_ac[0][0]', \n", " caleLayer) 'block8_7_conv[0][0]'] \n", " \n", " block8_7_ac (Activation) (None, 5, 5, 2080) 0 ['custom_scale_layer_36[0][0]'] \n", " \n", " conv2d_192 (Conv2D) (None, 5, 5, 192) 399360 ['block8_7_ac[0][0]'] \n", " \n", " batch_normalization_192 (Batch (None, 5, 5, 192) 576 ['conv2d_192[0][0]'] \n", " Normalization) \n", " \n", " activation_192 (Activation) (None, 5, 5, 192) 0 ['batch_normalization_192[0][0]']\n", " \n", " conv2d_193 (Conv2D) (None, 5, 5, 224) 129024 ['activation_192[0][0]'] \n", " \n", " batch_normalization_193 (Batch (None, 5, 5, 224) 672 ['conv2d_193[0][0]'] \n", " Normalization) \n", " \n", " activation_193 (Activation) (None, 5, 5, 224) 0 ['batch_normalization_193[0][0]']\n", " \n", " conv2d_191 (Conv2D) (None, 5, 5, 192) 399360 ['block8_7_ac[0][0]'] \n", " \n", " conv2d_194 (Conv2D) (None, 5, 5, 256) 172032 ['activation_193[0][0]'] \n", " \n", " batch_normalization_191 (Batch (None, 5, 5, 192) 576 ['conv2d_191[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_194 (Batch (None, 5, 5, 256) 768 ['conv2d_194[0][0]'] \n", " Normalization) \n", " \n", " activation_191 (Activation) (None, 5, 5, 192) 0 ['batch_normalization_191[0][0]']\n", " \n", " activation_194 (Activation) (None, 5, 5, 256) 0 ['batch_normalization_194[0][0]']\n", " \n", " block8_8_mixed (Concatenate) (None, 5, 5, 448) 0 ['activation_191[0][0]', \n", " 'activation_194[0][0]'] \n", " \n", " block8_8_conv (Conv2D) (None, 5, 5, 2080) 933920 ['block8_8_mixed[0][0]'] \n", " \n", " custom_scale_layer_37 (CustomS (None, 5, 5, 2080) 0 ['block8_7_ac[0][0]', \n", " caleLayer) 'block8_8_conv[0][0]'] \n", " \n", " block8_8_ac (Activation) (None, 5, 5, 2080) 0 ['custom_scale_layer_37[0][0]'] \n", " \n", " conv2d_196 (Conv2D) (None, 5, 5, 192) 399360 ['block8_8_ac[0][0]'] \n", " \n", " batch_normalization_196 (Batch (None, 5, 5, 192) 576 ['conv2d_196[0][0]'] \n", " Normalization) \n", " \n", " activation_196 (Activation) (None, 5, 5, 192) 0 ['batch_normalization_196[0][0]']\n", " \n", " conv2d_197 (Conv2D) (None, 5, 5, 224) 129024 ['activation_196[0][0]'] \n", " \n", " batch_normalization_197 (Batch (None, 5, 5, 224) 672 ['conv2d_197[0][0]'] \n", " Normalization) \n", " \n", " activation_197 (Activation) (None, 5, 5, 224) 0 ['batch_normalization_197[0][0]']\n", " \n", " conv2d_195 (Conv2D) (None, 5, 5, 192) 399360 ['block8_8_ac[0][0]'] \n", " \n", " conv2d_198 (Conv2D) (None, 5, 5, 256) 172032 ['activation_197[0][0]'] \n", " \n", " batch_normalization_195 (Batch (None, 5, 5, 192) 576 ['conv2d_195[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_198 (Batch (None, 5, 5, 256) 768 ['conv2d_198[0][0]'] \n", " Normalization) \n", " \n", " activation_195 (Activation) (None, 5, 5, 192) 0 ['batch_normalization_195[0][0]']\n", " \n", " activation_198 (Activation) (None, 5, 5, 256) 0 ['batch_normalization_198[0][0]']\n", " \n", " block8_9_mixed (Concatenate) (None, 5, 5, 448) 0 ['activation_195[0][0]', \n", " 'activation_198[0][0]'] \n", " \n", " block8_9_conv (Conv2D) (None, 5, 5, 2080) 933920 ['block8_9_mixed[0][0]'] \n", " \n", " custom_scale_layer_38 (CustomS (None, 5, 5, 2080) 0 ['block8_8_ac[0][0]', \n", " caleLayer) 'block8_9_conv[0][0]'] \n", " \n", " block8_9_ac (Activation) (None, 5, 5, 2080) 0 ['custom_scale_layer_38[0][0]'] \n", " \n", " conv2d_200 (Conv2D) (None, 5, 5, 192) 399360 ['block8_9_ac[0][0]'] \n", " \n", " batch_normalization_200 (Batch (None, 5, 5, 192) 576 ['conv2d_200[0][0]'] \n", " Normalization) \n", " \n", " activation_200 (Activation) (None, 5, 5, 192) 0 ['batch_normalization_200[0][0]']\n", " \n", " conv2d_201 (Conv2D) (None, 5, 5, 224) 129024 ['activation_200[0][0]'] \n", " \n", " batch_normalization_201 (Batch (None, 5, 5, 224) 672 ['conv2d_201[0][0]'] \n", " Normalization) \n", " \n", " activation_201 (Activation) (None, 5, 5, 224) 0 ['batch_normalization_201[0][0]']\n", " \n", " conv2d_199 (Conv2D) (None, 5, 5, 192) 399360 ['block8_9_ac[0][0]'] \n", " \n", " conv2d_202 (Conv2D) (None, 5, 5, 256) 172032 ['activation_201[0][0]'] \n", " \n", " batch_normalization_199 (Batch (None, 5, 5, 192) 576 ['conv2d_199[0][0]'] \n", " Normalization) \n", " \n", " batch_normalization_202 (Batch (None, 5, 5, 256) 768 ['conv2d_202[0][0]'] \n", " Normalization) \n", " \n", " activation_199 (Activation) (None, 5, 5, 192) 0 ['batch_normalization_199[0][0]']\n", " \n", " activation_202 (Activation) (None, 5, 5, 256) 0 ['batch_normalization_202[0][0]']\n", " \n", " block8_10_mixed (Concatenate) (None, 5, 5, 448) 0 ['activation_199[0][0]', \n", " 'activation_202[0][0]'] \n", " \n", " block8_10_conv (Conv2D) (None, 5, 5, 2080) 933920 ['block8_10_mixed[0][0]'] \n", " \n", " custom_scale_layer_39 (CustomS (None, 5, 5, 2080) 0 ['block8_9_ac[0][0]', \n", " caleLayer) 'block8_10_conv[0][0]'] \n", " \n", " conv_7b (Conv2D) (None, 5, 5, 1536) 3194880 ['custom_scale_layer_39[0][0]'] \n", " \n", " conv_7b_bn (BatchNormalization (None, 5, 5, 1536) 4608 ['conv_7b[0][0]'] \n", " ) \n", " \n", " conv_7b_ac (Activation) (None, 5, 5, 1536) 0 ['conv_7b_bn[0][0]'] \n", " \n", " flatten (Flatten) (None, 38400) 0 ['conv_7b_ac[0][0]'] \n", " \n", " dense (Dense) (None, 500) 19200500 ['flatten[0][0]'] \n", " \n", " dense_1 (Dense) (None, 250) 125250 ['dense[0][0]'] \n", " \n", " dense_2 (Dense) (None, 4) 1004 ['dense_1[0][0]'] \n", " \n", "==================================================================================================\n", "Total params: 73,663,490\n", "Trainable params: 73,602,946\n", "Non-trainable params: 60,544\n", "__________________________________________________________________________________________________\n" ] } ] }, { "cell_type": "code", "source": [ "tfb = TensorBoard('object_detection')\n", "history = model.fit(x=x_train,y=y_train,batch_size=10,epochs=150,\n", " validation_data=(x_test,y_test),callbacks=[tfb])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "cFo2wZjAJ_ST", "outputId": "ca3dd995-d3ee-4993-cbd1-6fdfb2fb56a6" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1/150\n", "35/35 [==============================] - 127s 463ms/step - loss: 0.0356 - val_loss: 0.0303\n", "Epoch 2/150\n", "35/35 [==============================] - 8s 227ms/step - loss: 0.0128 - val_loss: 0.0257\n", "Epoch 3/150\n", "35/35 [==============================] - 8s 231ms/step - loss: 0.0081 - val_loss: 0.0184\n", "Epoch 4/150\n", "35/35 [==============================] - 8s 230ms/step - loss: 0.0049 - val_loss: 0.0186\n", "Epoch 5/150\n", "35/35 [==============================] - 8s 232ms/step - loss: 0.0044 - val_loss: 0.0179\n", "Epoch 6/150\n", "35/35 [==============================] - 8s 232ms/step - loss: 0.0038 - val_loss: 0.0158\n", "Epoch 7/150\n", "35/35 [==============================] - 8s 230ms/step - loss: 0.0034 - val_loss: 0.0151\n", "Epoch 8/150\n", "35/35 [==============================] - 8s 230ms/step - loss: 0.0029 - val_loss: 0.0151\n", "Epoch 9/150\n", "35/35 [==============================] - 8s 230ms/step - loss: 0.0024 - val_loss: 0.0148\n", "Epoch 10/150\n", "35/35 [==============================] - 8s 229ms/step - loss: 0.0025 - val_loss: 0.0144\n", "Epoch 11/150\n", "35/35 [==============================] - 8s 236ms/step - loss: 0.0021 - val_loss: 0.0138\n", "Epoch 12/150\n", "35/35 [==============================] - 8s 233ms/step - loss: 0.0021 - val_loss: 0.0140\n", "Epoch 13/150\n", "35/35 [==============================] - 8s 239ms/step - loss: 0.0018 - val_loss: 0.0140\n", "Epoch 14/150\n", "35/35 [==============================] - 8s 239ms/step - loss: 0.0017 - val_loss: 0.0137\n", "Epoch 15/150\n", "35/35 [==============================] - 8s 240ms/step - loss: 0.0019 - val_loss: 0.0139\n", "Epoch 16/150\n", "35/35 [==============================] - 10s 286ms/step - loss: 0.0021 - val_loss: 0.0149\n", "Epoch 17/150\n", "35/35 [==============================] - 8s 237ms/step - loss: 0.0016 - val_loss: 0.0139\n", "Epoch 18/150\n", "35/35 [==============================] - 12s 335ms/step - loss: 0.0014 - val_loss: 0.0143\n", "Epoch 19/150\n", "35/35 [==============================] - 9s 255ms/step - loss: 0.0013 - val_loss: 0.0146\n", "Epoch 20/150\n", "35/35 [==============================] - 11s 304ms/step - loss: 0.0014 - val_loss: 0.0141\n", "Epoch 21/150\n", "35/35 [==============================] - 9s 247ms/step - loss: 0.0013 - val_loss: 0.0147\n", "Epoch 22/150\n", "35/35 [==============================] - 10s 280ms/step - loss: 0.0012 - val_loss: 0.0135\n", "Epoch 23/150\n", "35/35 [==============================] - 11s 304ms/step - loss: 0.0012 - val_loss: 0.0133\n", "Epoch 24/150\n", "35/35 [==============================] - 8s 236ms/step - loss: 0.0013 - val_loss: 0.0129\n", "Epoch 25/150\n", "35/35 [==============================] - 9s 255ms/step - loss: 0.0013 - val_loss: 0.0376\n", "Epoch 26/150\n", "35/35 [==============================] - 10s 297ms/step - loss: 0.0013 - val_loss: 0.0330\n", "Epoch 27/150\n", "35/35 [==============================] - 8s 235ms/step - loss: 0.0012 - val_loss: 0.0315\n", "Epoch 28/150\n", "35/35 [==============================] - 8s 236ms/step - loss: 0.0012 - val_loss: 0.0224\n", "Epoch 29/150\n", "35/35 [==============================] - 10s 285ms/step - loss: 0.0011 - val_loss: 0.0140\n", "Epoch 30/150\n", "35/35 [==============================] - 8s 241ms/step - loss: 0.0011 - val_loss: 0.0133\n", "Epoch 31/150\n", "35/35 [==============================] - 8s 238ms/step - loss: 0.0010 - val_loss: 0.0129\n", "Epoch 32/150\n", "35/35 [==============================] - 8s 236ms/step - loss: 0.0011 - val_loss: 0.0187\n", "Epoch 33/150\n", "35/35 [==============================] - 8s 239ms/step - loss: 0.0011 - val_loss: 0.0160\n", "Epoch 34/150\n", "35/35 [==============================] - 8s 244ms/step - loss: 0.0011 - val_loss: 0.0150\n", "Epoch 35/150\n", "35/35 [==============================] - 8s 239ms/step - loss: 0.0011 - val_loss: 0.0141\n", "Epoch 36/150\n", "35/35 [==============================] - 9s 266ms/step - loss: 0.0011 - val_loss: 0.0141\n", "Epoch 37/150\n", "35/35 [==============================] - 9s 264ms/step - loss: 0.0011 - val_loss: 0.0143\n", "Epoch 38/150\n", "35/35 [==============================] - 10s 283ms/step - loss: 0.0011 - val_loss: 0.0143\n", "Epoch 39/150\n", "35/35 [==============================] - 9s 261ms/step - loss: 0.0011 - val_loss: 0.0147\n", "Epoch 40/150\n", "35/35 [==============================] - 8s 243ms/step - loss: 0.0010 - val_loss: 0.0141\n", "Epoch 41/150\n", "35/35 [==============================] - 9s 267ms/step - loss: 0.0010 - val_loss: 0.0135\n", "Epoch 42/150\n", "35/35 [==============================] - 8s 238ms/step - loss: 0.0011 - val_loss: 0.0156\n", "Epoch 43/150\n", "35/35 [==============================] - 8s 238ms/step - loss: 0.0010 - val_loss: 0.0133\n", "Epoch 44/150\n", "35/35 [==============================] - 8s 235ms/step - loss: 9.9773e-04 - val_loss: 0.0132\n", "Epoch 45/150\n", "35/35 [==============================] - 8s 235ms/step - loss: 9.7563e-04 - val_loss: 0.0134\n", "Epoch 46/150\n", "35/35 [==============================] - 8s 238ms/step - loss: 9.7904e-04 - val_loss: 0.0138\n", "Epoch 47/150\n", "35/35 [==============================] - 8s 237ms/step - loss: 9.6317e-04 - val_loss: 0.0140\n", "Epoch 48/150\n", "35/35 [==============================] - 9s 269ms/step - loss: 9.0882e-04 - val_loss: 0.0138\n", "Epoch 49/150\n", "35/35 [==============================] - 9s 247ms/step - loss: 9.2736e-04 - val_loss: 0.0140\n", "Epoch 50/150\n", "35/35 [==============================] - 8s 238ms/step - loss: 9.3499e-04 - val_loss: 0.0138\n", "Epoch 51/150\n", "35/35 [==============================] - 8s 239ms/step - loss: 9.9434e-04 - val_loss: 0.0145\n", "Epoch 52/150\n", "35/35 [==============================] - 8s 233ms/step - loss: 9.5813e-04 - val_loss: 0.0144\n", "Epoch 53/150\n", "35/35 [==============================] - 8s 239ms/step - loss: 8.9544e-04 - val_loss: 0.0141\n", "Epoch 54/150\n", "35/35 [==============================] - 9s 252ms/step - loss: 9.4684e-04 - val_loss: 0.0151\n", "Epoch 55/150\n", "35/35 [==============================] - 8s 236ms/step - loss: 9.2408e-04 - val_loss: 0.0149\n", "Epoch 56/150\n", "35/35 [==============================] - 10s 276ms/step - loss: 9.4967e-04 - val_loss: 0.0140\n", "Epoch 57/150\n", "35/35 [==============================] - 8s 235ms/step - loss: 0.0014 - val_loss: 0.0139\n", "Epoch 58/150\n", "35/35 [==============================] - 8s 235ms/step - loss: 9.1836e-04 - val_loss: 0.0151\n", "Epoch 59/150\n", "35/35 [==============================] - 8s 237ms/step - loss: 9.2359e-04 - val_loss: 0.0156\n", "Epoch 60/150\n", "35/35 [==============================] - 8s 235ms/step - loss: 9.1893e-04 - val_loss: 0.0148\n", "Epoch 61/150\n", "35/35 [==============================] - 8s 236ms/step - loss: 8.8643e-04 - val_loss: 0.0157\n", "Epoch 62/150\n", "35/35 [==============================] - 8s 237ms/step - loss: 8.9998e-04 - val_loss: 0.0150\n", "Epoch 63/150\n", "35/35 [==============================] - 8s 235ms/step - loss: 8.8228e-04 - val_loss: 0.0135\n", "Epoch 64/150\n", "35/35 [==============================] - 8s 236ms/step - loss: 8.9206e-04 - val_loss: 0.0138\n", "Epoch 65/150\n", "35/35 [==============================] - 8s 235ms/step - loss: 9.3854e-04 - val_loss: 0.0136\n", "Epoch 66/150\n", "35/35 [==============================] - 8s 237ms/step - loss: 9.2827e-04 - val_loss: 0.0136\n", "Epoch 67/150\n", "35/35 [==============================] - 8s 238ms/step - loss: 9.3432e-04 - val_loss: 0.0138\n", "Epoch 68/150\n", "35/35 [==============================] - 8s 235ms/step - loss: 9.2129e-04 - val_loss: 0.0155\n", "Epoch 69/150\n", "35/35 [==============================] - 8s 237ms/step - loss: 8.9346e-04 - val_loss: 0.0146\n", "Epoch 70/150\n", "35/35 [==============================] - 8s 233ms/step - loss: 9.0590e-04 - val_loss: 0.0152\n", "Epoch 71/150\n", "35/35 [==============================] - 8s 239ms/step - loss: 8.9907e-04 - val_loss: 0.0153\n", "Epoch 72/150\n", "35/35 [==============================] - 8s 238ms/step - loss: 8.6129e-04 - val_loss: 0.0148\n", "Epoch 73/150\n", "35/35 [==============================] - 8s 236ms/step - loss: 8.6686e-04 - val_loss: 0.0149\n", "Epoch 74/150\n", "35/35 [==============================] - 8s 238ms/step - loss: 8.7622e-04 - val_loss: 0.0160\n", "Epoch 75/150\n", "35/35 [==============================] - 8s 237ms/step - loss: 9.1724e-04 - val_loss: 0.0156\n", "Epoch 76/150\n", "35/35 [==============================] - 8s 237ms/step - loss: 9.2280e-04 - val_loss: 0.0139\n", "Epoch 77/150\n", "35/35 [==============================] - 8s 240ms/step - loss: 9.9196e-04 - val_loss: 0.0156\n", "Epoch 78/150\n", "35/35 [==============================] - 8s 235ms/step - loss: 9.5622e-04 - val_loss: 0.0158\n", "Epoch 79/150\n", "35/35 [==============================] - 8s 238ms/step - loss: 9.1258e-04 - val_loss: 0.0136\n", "Epoch 80/150\n", "35/35 [==============================] - 8s 236ms/step - loss: 8.9626e-04 - val_loss: 0.0149\n", "Epoch 81/150\n", "35/35 [==============================] - 8s 237ms/step - loss: 8.8618e-04 - val_loss: 0.0154\n", "Epoch 82/150\n", "35/35 [==============================] - 8s 238ms/step - loss: 8.8918e-04 - val_loss: 0.0176\n", "Epoch 83/150\n", "35/35 [==============================] - 9s 249ms/step - loss: 9.6153e-04 - val_loss: 0.0166\n", "Epoch 84/150\n", "35/35 [==============================] - 8s 239ms/step - loss: 9.2918e-04 - val_loss: 0.0170\n", "Epoch 85/150\n", "35/35 [==============================] - 8s 239ms/step - loss: 9.2539e-04 - val_loss: 0.0163\n", "Epoch 86/150\n", "35/35 [==============================] - 10s 297ms/step - loss: 9.0356e-04 - val_loss: 0.0147\n", "Epoch 87/150\n", "35/35 [==============================] - 10s 284ms/step - loss: 9.7117e-04 - val_loss: 0.0140\n", "Epoch 88/150\n", "35/35 [==============================] - 8s 237ms/step - loss: 9.8083e-04 - val_loss: 0.0140\n", "Epoch 89/150\n", "35/35 [==============================] - 8s 234ms/step - loss: 9.4058e-04 - val_loss: 0.0137\n", "Epoch 90/150\n", "35/35 [==============================] - 11s 308ms/step - loss: 8.9453e-04 - val_loss: 0.0134\n", "Epoch 91/150\n", "35/35 [==============================] - 11s 311ms/step - loss: 9.0702e-04 - val_loss: 0.0154\n", "Epoch 92/150\n", "35/35 [==============================] - 11s 306ms/step - loss: 8.9170e-04 - val_loss: 0.0153\n", "Epoch 93/150\n", "35/35 [==============================] - 10s 260ms/step - loss: 8.5982e-04 - val_loss: 0.0171\n", "Epoch 94/150\n", "35/35 [==============================] - 10s 294ms/step - loss: 8.4258e-04 - val_loss: 0.0161\n", "Epoch 95/150\n", "35/35 [==============================] - 8s 239ms/step - loss: 8.2960e-04 - val_loss: 0.0163\n", "Epoch 96/150\n", "35/35 [==============================] - 8s 236ms/step - loss: 8.6837e-04 - val_loss: 0.0137\n", "Epoch 97/150\n", "35/35 [==============================] - 8s 239ms/step - loss: 8.8043e-04 - val_loss: 0.0137\n", "Epoch 98/150\n", "35/35 [==============================] - 8s 236ms/step - loss: 8.9199e-04 - val_loss: 0.0181\n", "Epoch 99/150\n", "35/35 [==============================] - 8s 238ms/step - loss: 9.6096e-04 - val_loss: 0.0174\n", "Epoch 100/150\n", "35/35 [==============================] - 8s 239ms/step - loss: 9.4274e-04 - val_loss: 0.0180\n", "Epoch 101/150\n", "35/35 [==============================] - 8s 235ms/step - loss: 8.9355e-04 - val_loss: 0.0151\n", "Epoch 102/150\n", "35/35 [==============================] - 8s 237ms/step - loss: 8.6018e-04 - val_loss: 0.0173\n", "Epoch 103/150\n", "35/35 [==============================] - 8s 238ms/step - loss: 8.7332e-04 - val_loss: 0.0169\n", "Epoch 104/150\n", "35/35 [==============================] - 8s 236ms/step - loss: 8.5630e-04 - val_loss: 0.0167\n", "Epoch 105/150\n", "35/35 [==============================] - 9s 264ms/step - loss: 8.6511e-04 - val_loss: 0.0165\n", "Epoch 106/150\n", "35/35 [==============================] - 8s 235ms/step - loss: 8.7511e-04 - val_loss: 0.0156\n", "Epoch 107/150\n", "35/35 [==============================] - 8s 237ms/step - loss: 8.9569e-04 - val_loss: 0.0150\n", "Epoch 108/150\n", "35/35 [==============================] - 8s 239ms/step - loss: 8.8268e-04 - val_loss: 0.0153\n", "Epoch 109/150\n", "35/35 [==============================] - 8s 235ms/step - loss: 8.5574e-04 - val_loss: 0.0178\n", "Epoch 110/150\n", "35/35 [==============================] - 8s 239ms/step - loss: 8.8773e-04 - val_loss: 0.0183\n", "Epoch 111/150\n", "35/35 [==============================] - 8s 237ms/step - loss: 8.7929e-04 - val_loss: 0.0150\n", "Epoch 112/150\n", "35/35 [==============================] - 8s 235ms/step - loss: 8.9902e-04 - val_loss: 0.0195\n", "Epoch 113/150\n", "35/35 [==============================] - 8s 239ms/step - loss: 8.7688e-04 - val_loss: 0.0205\n", "Epoch 114/150\n", "35/35 [==============================] - 8s 233ms/step - loss: 8.7218e-04 - val_loss: 0.0141\n", "Epoch 115/150\n", "35/35 [==============================] - 10s 301ms/step - loss: 8.5935e-04 - val_loss: 0.0170\n", "Epoch 116/150\n", "35/35 [==============================] - 8s 240ms/step - loss: 8.4702e-04 - val_loss: 0.0178\n", "Epoch 117/150\n", "35/35 [==============================] - 8s 235ms/step - loss: 9.2165e-04 - val_loss: 0.0178\n", "Epoch 118/150\n", "35/35 [==============================] - 8s 238ms/step - loss: 8.7197e-04 - val_loss: 0.0153\n", "Epoch 119/150\n", "35/35 [==============================] - 8s 238ms/step - loss: 8.6966e-04 - val_loss: 0.0136\n", "Epoch 120/150\n", "35/35 [==============================] - 8s 235ms/step - loss: 8.8293e-04 - val_loss: 0.0143\n", "Epoch 121/150\n", "35/35 [==============================] - 8s 238ms/step - loss: 8.6402e-04 - val_loss: 0.0162\n", "Epoch 122/150\n", "35/35 [==============================] - 8s 233ms/step - loss: 8.8162e-04 - val_loss: 0.0178\n", "Epoch 123/150\n", "35/35 [==============================] - 8s 238ms/step - loss: 8.6219e-04 - val_loss: 0.0244\n", "Epoch 124/150\n", "35/35 [==============================] - 8s 239ms/step - loss: 8.5740e-04 - val_loss: 0.0227\n", "Epoch 125/150\n", "35/35 [==============================] - 8s 236ms/step - loss: 8.8429e-04 - val_loss: 0.0177\n", "Epoch 126/150\n", "35/35 [==============================] - 8s 238ms/step - loss: 9.1810e-04 - val_loss: 0.0138\n", "Epoch 127/150\n", "35/35 [==============================] - 8s 234ms/step - loss: 8.7023e-04 - val_loss: 0.0129\n", "Epoch 128/150\n", "35/35 [==============================] - 8s 238ms/step - loss: 8.7421e-04 - val_loss: 0.0157\n", "Epoch 129/150\n", "35/35 [==============================] - 8s 237ms/step - loss: 8.8710e-04 - val_loss: 0.0154\n", "Epoch 130/150\n", "35/35 [==============================] - 8s 235ms/step - loss: 8.8209e-04 - val_loss: 0.0131\n", "Epoch 131/150\n", "35/35 [==============================] - 8s 235ms/step - loss: 9.4106e-04 - val_loss: 0.0129\n", "Epoch 132/150\n", "35/35 [==============================] - 8s 237ms/step - loss: 9.0076e-04 - val_loss: 0.0148\n", "Epoch 133/150\n", "35/35 [==============================] - 8s 236ms/step - loss: 9.1871e-04 - val_loss: 0.0127\n", "Epoch 134/150\n", "35/35 [==============================] - 8s 240ms/step - loss: 8.9399e-04 - val_loss: 0.0155\n", "Epoch 135/150\n", "35/35 [==============================] - 8s 236ms/step - loss: 8.5873e-04 - val_loss: 0.0142\n", "Epoch 136/150\n", "35/35 [==============================] - 8s 238ms/step - loss: 8.8720e-04 - val_loss: 0.0164\n", "Epoch 137/150\n", "35/35 [==============================] - 8s 238ms/step - loss: 9.7503e-04 - val_loss: 0.0194\n", "Epoch 138/150\n", "35/35 [==============================] - 8s 235ms/step - loss: 9.2702e-04 - val_loss: 0.0203\n", "Epoch 139/150\n", "35/35 [==============================] - 8s 236ms/step - loss: 9.4280e-04 - val_loss: 0.0145\n", "Epoch 140/150\n", "35/35 [==============================] - 8s 234ms/step - loss: 8.8511e-04 - val_loss: 0.0143\n", "Epoch 141/150\n", "35/35 [==============================] - 8s 237ms/step - loss: 9.0632e-04 - val_loss: 0.0208\n", "Epoch 142/150\n", "35/35 [==============================] - 8s 238ms/step - loss: 8.9365e-04 - val_loss: 0.0193\n", "Epoch 143/150\n", "35/35 [==============================] - 8s 234ms/step - loss: 0.0010 - val_loss: 0.0162\n", "Epoch 144/150\n", "35/35 [==============================] - 8s 237ms/step - loss: 9.8777e-04 - val_loss: 0.0158\n", "Epoch 145/150\n", "35/35 [==============================] - 8s 236ms/step - loss: 9.8738e-04 - val_loss: 0.0167\n", "Epoch 146/150\n", "35/35 [==============================] - 8s 237ms/step - loss: 0.0010 - val_loss: 0.0153\n", "Epoch 147/150\n", "35/35 [==============================] - 8s 237ms/step - loss: 9.5996e-04 - val_loss: 0.0163\n", "Epoch 148/150\n", "35/35 [==============================] - 8s 234ms/step - loss: 9.6427e-04 - val_loss: 0.0117\n", "Epoch 149/150\n", "35/35 [==============================] - 8s 237ms/step - loss: 9.3221e-04 - val_loss: 0.0125\n", "Epoch 150/150\n", "35/35 [==============================] - 8s 237ms/step - loss: 9.9623e-04 - val_loss: 0.0154\n" ] } ] }, { "cell_type": "code", "source": [ "model.save('object_detection.h5')" ], "metadata": { "id": "OW8jUSDaJ_VO" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "history.history['loss']" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mLoE61QYJ_YB", "outputId": "13374733-64b0-4df3-c59c-eb0bd4d4ccf1" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[0.044009752571582794,\n", " 0.012237345799803734,\n", " 0.0060814376920461655,\n", " 0.0041739121079444885,\n", " 0.003680601716041565,\n", " 0.0029068654403090477,\n", " 0.002822810783982277,\n", " 0.009261803701519966,\n", " 0.0044119395315647125,\n", " 0.002808129880577326,\n", " 0.002093474380671978,\n", " 0.001990886405110359,\n", " 0.001589433872140944,\n", " 0.0016271985368803144,\n", " 0.001693879603408277,\n", " 0.001471603405661881,\n", " 0.0013716027606278658,\n", " 0.0014273056294769049,\n", " 0.0014788012485951185,\n", " 0.0013237260282039642,\n", " 0.001286126091144979,\n", " 0.0013030716218054295,\n", " 0.0011910900939255953,\n", " 0.001103530521504581,\n", " 0.0011039761593565345,\n", " 0.001091974787414074,\n", " 0.0011978308903053403,\n", " 0.0010677026584744453,\n", " 0.0010926596587523818,\n", " 0.0011444431729614735,\n", " 0.0011224320624023676,\n", " 0.0010568638099357486,\n", " 0.0010580049129202962,\n", " 0.0009636334725655615,\n", " 0.0009815423982217908,\n", " 0.0009734889608807862,\n", " 0.0009148276876658201,\n", " 0.0009474341641180217,\n", " 0.0010352198733016849,\n", " 0.0010098246857523918,\n", " 0.000909763271920383,\n", " 0.0009641414508223534,\n", " 0.0009369482286274433,\n", " 0.0009241461521014571,\n", " 0.0009536270517855883,\n", " 0.00091659085592255,\n", " 0.0008920723921619356,\n", " 0.0009528407827019691,\n", " 0.0009628439438529313,\n", " 0.0009192124125547707,\n", " 0.0008989464258775115,\n", " 0.0009420680580660701,\n", " 0.0009529261733405292,\n", " 0.0009559787577018142,\n", " 0.0009479639120399952,\n", " 0.0009287189459428191,\n", " 0.0009188604890368879,\n", " 0.0008905348950065672,\n", " 0.0009164174553006887,\n", " 0.000912004616111517,\n", " 0.0008913533529266715,\n", " 0.0009331773617304862,\n", " 0.000923031650017947,\n", " 0.0008982804138213396,\n", " 0.0009456357220187783,\n", " 0.000923014129512012,\n", " 0.0009438241249881685,\n", " 0.0008851204765960574,\n", " 0.0008658822625875473,\n", " 0.0008745572413317859,\n", " 0.0008859694935381413,\n", " 0.0009094827109947801,\n", " 0.0008923989371396601,\n", " 0.0008766017272137105,\n", " 0.0008796403417363763,\n", " 0.0008400942897424102,\n", " 0.0008675367571413517,\n", " 0.000869082985445857,\n", " 0.0009136301814578474,\n", " 0.0008925662841647863,\n", " 0.0008924337453208864,\n", " 0.0008751396089792252,\n", " 0.0008986371103674173,\n", " 0.0009154239087365568,\n", " 0.0008799598435871303,\n", " 0.0008375982288271189,\n", " 0.0008547243778593838,\n", " 0.0008491293410770595,\n", " 0.0008726129308342934,\n", " 0.0008743899525143206,\n", " 0.0008895250502973795,\n", " 0.0008916217484511435,\n", " 0.000899301958270371,\n", " 0.0008598810527473688,\n", " 0.0008915939833968878,\n", " 0.0008717426680959761,\n", " 0.0008798433700576425,\n", " 0.0008554323576390743,\n", " 0.000849340867716819,\n", " 0.0008594862301833928,\n", " 0.0008673008996993303,\n", " 0.0008688251255080104,\n", " 0.0008626799681223929,\n", " 0.0008707590168341994,\n", " 0.0008967743488028646,\n", " 0.0009339234093204141,\n", " 0.0009008394554257393,\n", " 0.0008948088507167995,\n", " 0.0010305056348443031,\n", " 0.0009483005851507187,\n", " 0.0009292303584516048,\n", " 0.0008855756605044007,\n", " 0.0008741198689676821,\n", " 0.0009087196667678654,\n", " 0.0008474229252897203,\n", " 0.0008498624665662646,\n", " 0.0008565390598960221,\n", " 0.0008460409007966518,\n", " 0.0008500087424181402,\n", " 0.0008237061556428671,\n", " 0.0008493943605571985,\n", " 0.0008708285167813301,\n", " 0.0008435621275566518,\n", " 0.0009509380324743688,\n", " 0.0009153939317911863,\n", " 0.0010421164333820343,\n", " 0.0012300837552174926,\n", " 0.0010967941489070654,\n", " 0.0010260666022077203,\n", " 0.000964664330240339,\n", " 0.0009827729081735015,\n", " 0.000954245391767472,\n", " 0.000985597725957632,\n", " 0.0008778797928243876,\n", " 0.0008425344130955637,\n", " 0.0008278121240437031,\n", " 0.0011124919401481748,\n", " 0.0008960310369729996,\n", " 0.0008885048446245492,\n", " 0.000868993520271033,\n", " 0.0008444789564236999,\n", " 0.0008473162306472659,\n", " 0.0008533619111403823,\n", " 0.0008929461473599076,\n", " 0.0009955840650945902,\n", " 0.0008522277930751443,\n", " 0.0008499841205775738,\n", " 0.0009162991773337126,\n", " 0.0008697991142980754,\n", " 0.0008466712315566838,\n", " 0.0008555062231607735,\n", " 0.0008390759467147291,\n", " 0.0008379351929761469,\n", " 0.0008479400421492755,\n", " 0.0008380458457395434,\n", " 0.0008084018481895328,\n", " 0.0007937909686006606,\n", " 0.0007925775134935975,\n", " 0.0008437518263235688,\n", " 0.0008953254437074065,\n", " 0.0008745162631385028,\n", " 0.0008403642568737268,\n", " 0.000844281108584255,\n", " 0.0008220694726333022,\n", " 0.0009057290735654533,\n", " 0.0008973412332125008,\n", " 0.0009028022759594023,\n", " 0.0009000376448966563,\n", " 0.0008842604584060609,\n", " 0.0008533390355296433,\n", " 0.0009121723705902696,\n", " 0.0008825909462757409,\n", " 0.0008705538348294795,\n", " 0.0008556129760108888,\n", " 0.0008037930238060653,\n", " 0.0008244578493759036,\n", " 0.0008084814762696624,\n", " 0.0008079986437223852,\n", " 0.000798882800154388,\n", " 0.0008245054632425308]" ] }, "metadata": {}, "execution_count": 18 } ] }, { "cell_type": "code", "source": [ "history.history['val_loss']" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7M1Jp7K4Ymoo", "outputId": "75319533-34cf-4634-87e3-fd4bcf256568" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[0.03939463570713997,\n", " 0.02495860867202282,\n", " 0.020490333437919617,\n", " 0.018490970134735107,\n", " 0.017224816605448723,\n", " 0.017282726243138313,\n", " 0.018129102885723114,\n", " 0.01854594238102436,\n", " 0.016385501250624657,\n", " 0.01496779266744852,\n", " 0.015516441315412521,\n", " 0.014880947768688202,\n", " 0.015015779994428158,\n", " 0.015007940120995045,\n", " 0.014743130654096603,\n", " 0.01508299820125103,\n", " 0.015097996219992638,\n", " 0.017389852553606033,\n", " 0.016637949272990227,\n", " 0.01499248668551445,\n", " 0.014914975501596928,\n", " 0.016057219356298447,\n", " 0.016099143773317337,\n", " 0.015638677403330803,\n", " 0.014152036048471928,\n", " 0.014870123006403446,\n", " 0.01522839069366455,\n", " 0.014708824455738068,\n", " 0.01477177906781435,\n", " 0.014280254952609539,\n", " 0.015386302024126053,\n", " 0.015737276524305344,\n", " 0.015510686673223972,\n", " 0.016855549067258835,\n", " 0.018925776705145836,\n", " 0.01898052729666233,\n", " 0.019218187779188156,\n", " 0.018456503748893738,\n", " 0.01913103647530079,\n", " 0.01830332912504673,\n", " 0.01812063716351986,\n", " 0.017755351960659027,\n", " 0.015690501779317856,\n", " 0.015142938122153282,\n", " 0.015659935772418976,\n", " 0.015285101719200611,\n", " 0.015086473897099495,\n", " 0.015073098242282867,\n", " 0.015443496406078339,\n", " 0.015455980785191059,\n", " 0.015736211091279984,\n", " 0.01455734670162201,\n", " 0.016428092494606972,\n", " 0.013952948153018951,\n", " 0.013727518729865551,\n", " 0.013836720958352089,\n", " 0.014950578100979328,\n", " 0.014679915271699429,\n", " 0.01475087646394968,\n", " 0.015362399630248547,\n", " 0.014733348973095417,\n", " 0.015450931154191494,\n", " 0.01532560121268034,\n", " 0.014374037273228168,\n", " 0.014369379729032516,\n", " 0.015683678910136223,\n", " 0.01439700461924076,\n", " 0.013879653066396713,\n", " 0.015962772071361542,\n", " 0.015876969322562218,\n", " 0.016191480681300163,\n", " 0.01599704474210739,\n", " 0.016141017898917198,\n", " 0.015638185665011406,\n", " 0.018481159582734108,\n", " 0.017962362617254257,\n", " 0.013670539483428001,\n", " 0.014305347576737404,\n", " 0.014513492584228516,\n", " 0.014954942278563976,\n", " 0.014701648615300655,\n", " 0.015475846827030182,\n", " 0.014893662184476852,\n", " 0.014533479698002338,\n", " 0.014158613048493862,\n", " 0.015423433855175972,\n", " 0.013911851681768894,\n", " 0.0135303670540452,\n", " 0.01467879582196474,\n", " 0.014526867307722569,\n", " 0.014078747481107712,\n", " 0.013390671461820602,\n", " 0.013516996055841446,\n", " 0.013960235752165318,\n", " 0.014476817101240158,\n", " 0.014544039033353329,\n", " 0.01944744400680065,\n", " 0.019633455201983452,\n", " 0.017805133014917374,\n", " 0.014737730845808983,\n", " 0.01534220576286316,\n", " 0.013326220214366913,\n", " 0.013929716311395168,\n", " 0.018005570396780968,\n", " 0.013753675855696201,\n", " 0.01646421104669571,\n", " 0.015653027221560478,\n", " 0.014646565541625023,\n", " 0.016674216836690903,\n", " 0.01522902213037014,\n", " 0.015458689071238041,\n", " 0.015035368502140045,\n", " 0.014103086665272713,\n", " 0.014105839654803276,\n", " 0.013446205295622349,\n", " 0.013676396571099758,\n", " 0.013493258506059647,\n", " 0.013708885759115219,\n", " 0.01424927543848753,\n", " 0.016881410032510757,\n", " 0.015290085226297379,\n", " 0.013447161763906479,\n", " 0.01632595993578434,\n", " 0.02228923700749874,\n", " 0.018169043585658073,\n", " 0.016064466908574104,\n", " 0.012289809063076973,\n", " 0.01689697988331318,\n", " 0.014029712416231632,\n", " 0.013746999204158783,\n", " 0.013000624254345894,\n", " 0.016171958297491074,\n", " 0.015476410277187824,\n", " 0.01816602423787117,\n", " 0.015917189419269562,\n", " 0.014793864451348782,\n", " 0.013560446910560131,\n", " 0.012748386710882187,\n", " 0.012330839410424232,\n", " 0.012580671347677708,\n", " 0.01257319562137127,\n", " 0.01574564538896084,\n", " 0.013180513866245747,\n", " 0.013631856068968773,\n", " 0.015130977146327496,\n", " 0.014391268603503704,\n", " 0.013196262530982494,\n", " 0.01653810776770115,\n", " 0.017107810825109482,\n", " 0.01498645730316639,\n", " 0.013286064378917217,\n", " 0.012879520654678345,\n", " 0.01213578786700964,\n", " 0.012401295825839043,\n", " 0.013670883141458035,\n", " 0.013271290808916092,\n", " 0.012757953256368637,\n", " 0.013548344373703003,\n", " 0.01426171138882637,\n", " 0.013838943094015121,\n", " 0.019908687099814415,\n", " 0.015807485207915306,\n", " 0.017223723232746124,\n", " 0.01434921845793724,\n", " 0.020349644124507904,\n", " 0.018154529854655266,\n", " 0.015465236268937588,\n", " 0.015821369364857674,\n", " 0.01635773293673992,\n", " 0.012119843624532223,\n", " 0.012147659435868263,\n", " 0.012063911184668541,\n", " 0.015048961155116558,\n", " 0.01690077595412731,\n", " 0.016211330890655518,\n", " 0.014793855138123035,\n", " 0.013631526380777359,\n", " 0.01435928326100111,\n", " 0.01786760985851288,\n", " 0.019154516980051994]" ] }, "metadata": {}, "execution_count": 19 } ] }, { "cell_type": "code", "source": [ "path = '/content/archive (3)/car101.jpg'\n", "image_obj = load_img(path) # PIL object\n", "image = np.array(image_obj,dtype=np.uint8) # 8 bit array (0,255)\n", "image1 = load_img(path,target_size=(224,224))\n", "image_arr_224 = img_to_array(image1)/255.0 # Convert into array and get the normalized output\n", "test_arr = image_arr_224.reshape(1,224,224,3)\n", "\n", "h,w,d = image.shape\n", "# Make predictions\n", "coords = model.predict(test_arr)\n", "print(coords)\n", "# Denormalize the values\n", "denorm = np.array([w,w,h,h])\n", "coords = coords * denorm\n", "\n", "coords = coords.astype(np.int32)\n", "\n", "# Draw bounding on top the image\n", "xmin, xmax,ymin,ymax = coords[0]\n", "\n", "print(xmin, xmax, ymin,ymax)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "q9zkbou2RaWe", "outputId": "92a5572d-218a-4c05-df43-91048cb3e100" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "1/1 [==============================] - 0s 73ms/step\n", "[[0.17574842 0.3231801 0.4669857 0.5725074 ]]\n", "140 258 280 343\n" ] } ] }, { "cell_type": "code", "source": [ "path = '/content/archive (3)/car3.jpg'\n", "img = io.imread(path)\n", "plt.imshow(img)\n", "# rect = plt.Rectangle((xmin, ymin), xmax-xmin, ymax-ymin, color='r', fill=False)\n", "rect = plt.Rectangle((335, 200), 80, 30, color='r', fill=False)\n", "plt.gca().add_patch(rect)\n", "\n", "plt.show()\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 383 }, "id": "v7ACoeShQSyd", "outputId": "903d6854-6dcd-41e6-f862-267241cfa82d" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "0bVr0F-SQTCx" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "QMipWO7sQTGv" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## 4.train" ], "metadata": { "id": "HQloIibgKyyr" } }, { "cell_type": "code", "source": [ "# Load model\n", "model = tf.keras.models.load_model('object_detection.h5')\n", "print('Model loaded Sucessfully')" ], "metadata": { "id": "Ic9-GEqIK147" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "path = '../input/number-plate-detection/TEST/TEST.jpeg'\n", "image = load_img(path) # PIL object\n", "image = np.array(image,dtype=np.uint8) # 8 bit array (0,255)\n", "image1 = load_img(path,target_size=(224,224))\n", "image_arr_224 = img_to_array(image1)/255.0 # Convert into array and get the normalized output\n", "\n", "# Size of the orginal image\n", "h,w,d = image.shape\n", "print('Height of the image =',h)\n", "print('Width of the image =',w)" ], "metadata": { "id": "YcKXO24EK2BC" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "fig = px.imshow(image)\n", "fig.update_layout(width=700, height=500, margin=dict(l=10, r=10, b=10, t=10), xaxis_title='Figure 13 - TEST Image')" ], "metadata": { "id": "X1GdwX16K2Im" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "image_arr_224.shape" ], "metadata": { "id": "GmyotF9dLHHG" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "test_arr = image_arr_224.reshape(1,224,224,3)\n", "test_arr.shape" ], "metadata": { "id": "0E4TRFhVLHKB" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Make predictions\n", "coords = model.predict(test_arr)\n", "coords" ], "metadata": { "id": "98RFt6fBLHNK" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Denormalize the values\n", "denorm = np.array([w,w,h,h])\n", "coords = coords * denorm\n", "coords" ], "metadata": { "id": "rjsXT9NNLNAn" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "coords = coords.astype(np.int32)\n", "coords" ], "metadata": { "id": "TE4PMBa3LNDX" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Draw bounding on top the image\n", "xmin, xmax,ymin,ymax = coords[0]\n", "pt1 =(xmin,ymin)\n", "pt2 =(xmax,ymax)\n", "print(pt1, pt2)" ], "metadata": { "id": "oFJM9eJ9LNGP" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "cv2.rectangle(image,pt1,pt2,(0,255,0),3)\n", "fig = px.imshow(image)\n", "fig.update_layout(width=700, height=500, margin=dict(l=10, r=10, b=10, t=10))" ], "metadata": { "id": "qeOt4oEWLTib" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Create pipeline\n", "path = '../input/number-plate-detection/TEST/TEST.jpeg'\n", "def object_detection(path):\n", "\n", " # Read image\n", " image = load_img(path) # PIL object\n", " image = np.array(image,dtype=np.uint8) # 8 bit array (0,255)\n", " image1 = load_img(path,target_size=(224,224))\n", "\n", " # Data preprocessing\n", " image_arr_224 = img_to_array(image1)/255.0 # Convert to array & normalized\n", " h,w,d = image.shape\n", " test_arr = image_arr_224.reshape(1,224,224,3)\n", "\n", " # Make predictions\n", " coords = model.predict(test_arr)\n", "\n", " # Denormalize the values\n", " denorm = np.array([w,w,h,h])\n", " coords = coords * denorm\n", " coords = coords.astype(np.int32)\n", " # Draw bounding on top the image\n", " xmin, xmax,ymin,ymax = coords[0]\n", " pt1 =(xmin,ymin)\n", " pt2 =(xmax,ymax)\n", " print(pt1, pt2)\n", " cv2.rectangle(image,pt1,pt2,(0,255,0),3)\n", " return image, coords\n", "\n", "image, cods = object_detection(path)\n", "\n", "fig = px.imshow(image)\n", "fig.update_layout(width=700, height=500, margin=dict(l=10, r=10, b=10, t=10),xaxis_title='Figure 14')" ], "metadata": { "id": "EcoxIiJfLTlG" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "Pz7je1-DLTnv" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## 5.plt" ], "metadata": { "id": "nqNtkCwDb0nk" } }, { "cell_type": "code", "source": [ "# parsing\n", "def parsing(path):\n", " parser = xet.parse(path).getroot()\n", " name = parser.find('filename').text\n", " filename = f'/content/images/{name}'\n", "\n", " # width and height\n", " parser_size = parser.find('size')\n", " width = int(parser_size.find('width').text)\n", " height = int(parser_size.find('height').text)\n", "\n", " return filename, width, height\n", "df[['filename','width','height']] = df['filepath'].apply(parsing).apply(pd.Series)\n", "df.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "6mESvUj9b_mz", "outputId": "89841ab0-c968-493f-e404-724dde89309e" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " filepath xmin xmax ymin ymax \\\n", "0 /content/annotations/Cars420.xml 112 160 186 211 \n", "1 /content/annotations/Cars380.xml 316 364 91 126 \n", "2 /content/annotations/Cars232.xml 175 228 174 201 \n", "3 /content/annotations/Cars89.xml 307 345 206 221 \n", "4 /content/annotations/Cars221.xml 102 125 122 135 \n", "\n", " filename width height \n", "0 /content/images/Cars420.png 400 300 \n", "1 /content/images/Cars380.png 400 225 \n", "2 /content/images/Cars232.png 400 300 \n", "3 /content/images/Cars89.png 400 262 \n", "4 /content/images/Cars221.png 400 225 " ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
filepathxminxmaxyminymaxfilenamewidthheight
0/content/annotations/Cars420.xml112160186211/content/images/Cars420.png400300
1/content/annotations/Cars380.xml31636491126/content/images/Cars380.png400225
2/content/annotations/Cars232.xml175228174201/content/images/Cars232.png400300
3/content/annotations/Cars89.xml307345206221/content/images/Cars89.png400262
4/content/annotations/Cars221.xml102125122135/content/images/Cars221.png400225
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 11 } ] }, { "cell_type": "code", "source": [ "# center_x, center_y, width , height\n", "df['center_x'] = (df['xmax'] + df['xmin'])/(2*df['width'])\n", "df['center_y'] = (df['ymax'] + df['ymin'])/(2*df['height'])\n", "\n", "df['bb_width'] = (df['xmax'] - df['xmin'])/df['width']\n", "df['bb_height'] = (df['ymax'] - df['ymin'])/df['height']\n", "df.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "QAjDwKWtb1wo", "outputId": "1ba9d35b-978f-45b4-a430-9aa83c16944c" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " filepath xmin xmax ymin ymax \\\n", "0 /content/annotations/Cars420.xml 112 160 186 211 \n", "1 /content/annotations/Cars380.xml 316 364 91 126 \n", "2 /content/annotations/Cars232.xml 175 228 174 201 \n", "3 /content/annotations/Cars89.xml 307 345 206 221 \n", "4 /content/annotations/Cars221.xml 102 125 122 135 \n", "\n", " filename width height center_x center_y bb_width \\\n", "0 /content/images/Cars420.png 400 300 0.34000 0.661667 0.1200 \n", "1 /content/images/Cars380.png 400 225 0.85000 0.482222 0.1200 \n", "2 /content/images/Cars232.png 400 300 0.50375 0.625000 0.1325 \n", "3 /content/images/Cars89.png 400 262 0.81500 0.814885 0.0950 \n", "4 /content/images/Cars221.png 400 225 0.28375 0.571111 0.0575 \n", "\n", " bb_height \n", "0 0.083333 \n", "1 0.155556 \n", "2 0.090000 \n", "3 0.057252 \n", "4 0.057778 " ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
filepathxminxmaxyminymaxfilenamewidthheightcenter_xcenter_ybb_widthbb_height
0/content/annotations/Cars420.xml112160186211/content/images/Cars420.png4003000.340000.6616670.12000.083333
1/content/annotations/Cars380.xml31636491126/content/images/Cars380.png4002250.850000.4822220.12000.155556
2/content/annotations/Cars232.xml175228174201/content/images/Cars232.png4003000.503750.6250000.13250.090000
3/content/annotations/Cars89.xml307345206221/content/images/Cars89.png4002620.815000.8148850.09500.057252
4/content/annotations/Cars221.xml102125122135/content/images/Cars221.png4002250.283750.5711110.05750.057778
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", " \n", "
\n", "
\n", "
\n" ] }, "metadata": {}, "execution_count": 12 } ] }, { "cell_type": "code", "source": [ "# Aggregation Frequency loss 1000\n", "\n", "x = range(len(list1))\n", "plt.figure(figsize=(8,6))\n", "plt.plot(x, list1)\n", "plt.plot(x, list2)\n", "plt.grid(color=\"k\", linestyle=\":\")\n", "plt.legend(['valid loss', 'test loss'], loc='upper right')\n", "plt.ylabel('Loss', fontsize=15)\n", "plt.title('loss of train', fontsize=18)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 550 }, "id": "5Zyj_aUI4M-t", "outputId": "78c96bb6-867e-43c1-9d61-40c033325f5f" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] } ] }